Overview

Dataset statistics

Number of variables29
Number of observations239677
Missing cells1263226
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.4 MiB
Average record size in memory225.0 B

Variable types

Numeric9
DateTime1
Text18
Boolean1

Alerts

incident_url_fields_missing has constant value ""Constant
state_house_district is highly overall correlated with state_senate_districtHigh correlation
state_senate_district is highly overall correlated with state_house_districtHigh correlation
address has 16497 (6.9%) missing valuesMissing
congressional_district has 11944 (5.0%) missing valuesMissing
gun_stolen has 99498 (41.5%) missing valuesMissing
gun_type has 99451 (41.5%) missing valuesMissing
latitude has 7923 (3.3%) missing valuesMissing
location_description has 197588 (82.4%) missing valuesMissing
longitude has 7923 (3.3%) missing valuesMissing
n_guns_involved has 99451 (41.5%) missing valuesMissing
notes has 81017 (33.8%) missing valuesMissing
participant_age has 92298 (38.5%) missing valuesMissing
participant_age_group has 42119 (17.6%) missing valuesMissing
participant_gender has 36362 (15.2%) missing valuesMissing
participant_name has 122253 (51.0%) missing valuesMissing
participant_relationship has 223903 (93.4%) missing valuesMissing
participant_status has 27626 (11.5%) missing valuesMissing
participant_type has 24863 (10.4%) missing valuesMissing
state_house_district has 38772 (16.2%) missing valuesMissing
state_senate_district has 32335 (13.5%) missing valuesMissing
n_guns_involved is highly skewed (γ1 = 51.5950726)Skewed
incident_id has unique valuesUnique
incident_url has unique valuesUnique
n_killed has 185835 (77.5%) zerosZeros
n_injured has 142487 (59.4%) zerosZeros

Reproduction

Analysis started2023-11-25 21:18:32.577077
Analysis finished2023-11-25 21:19:21.453578
Duration48.88 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

incident_id
Real number (ℝ)

UNIQUE 

Distinct239677
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean559334.35
Minimum92114
Maximum1083472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:21.591862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum92114
5-th percentile126858.6
Q1308545
median543587
Q3817228
95-th percentile1028801.6
Maximum1083472
Range991358
Interquartile range (IQR)508683

Descriptive statistics

Standard deviation293128.68
Coefficient of variation (CV)0.52406702
Kurtosis-1.2241713
Mean559334.35
Median Absolute Deviation (MAD)253242
Skewness0.095167904
Sum1.3405958 × 1011
Variance8.5924426 × 1010
MonotonicityNot monotonic
2023-11-25T22:19:21.789421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461105 1
 
< 0.1%
717982 1
 
< 0.1%
718485 1
 
< 0.1%
718520 1
 
< 0.1%
719602 1
 
< 0.1%
718791 1
 
< 0.1%
717783 1
 
< 0.1%
717393 1
 
< 0.1%
717685 1
 
< 0.1%
719638 1
 
< 0.1%
Other values (239667) 239667
> 99.9%
ValueCountFrequency (%)
92114 1
< 0.1%
92117 1
< 0.1%
92119 1
< 0.1%
92122 1
< 0.1%
92125 1
< 0.1%
92129 1
< 0.1%
92131 1
< 0.1%
92133 1
< 0.1%
92135 1
< 0.1%
92137 1
< 0.1%
ValueCountFrequency (%)
1083472 1
< 0.1%
1083466 1
< 0.1%
1083457 1
< 0.1%
1083435 1
< 0.1%
1083428 1
< 0.1%
1083413 1
< 0.1%
1083396 1
< 0.1%
1083390 1
< 0.1%
1083389 1
< 0.1%
1083379 1
< 0.1%

date
Date

Distinct1725
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2013-01-01 00:00:00
Maximum2018-03-31 00:00:00
2023-11-25T22:19:21.977348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:22.338279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:22.603511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length20
Median length13
Mean length8.6643107
Min length4

Characters and Unicode

Total characters2076636
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPennsylvania
2nd rowCalifornia
3rd rowOhio
4th rowColorado
5th rowNorth Carolina
ValueCountFrequency (%)
new 17708
 
6.3%
illinois 17556
 
6.2%
california 16306
 
5.8%
carolina 15678
 
5.5%
florida 15029
 
5.3%
texas 13577
 
4.8%
ohio 10244
 
3.6%
york 9712
 
3.4%
north 9312
 
3.3%
pennsylvania 8929
 
3.2%
Other values (45) 148989
52.6%
2023-11-25T22:19:23.058785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 256726
12.4%
i 246073
11.8%
n 176038
 
8.5%
o 171176
 
8.2%
s 155405
 
7.5%
e 121747
 
5.9%
r 118926
 
5.7%
l 116103
 
5.6%
t 56154
 
2.7%
h 49637
 
2.4%
Other values (36) 608651
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1753428
84.4%
Uppercase Letter 279845
 
13.5%
Space Separator 43363
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 256726
14.6%
i 246073
14.0%
n 176038
10.0%
o 171176
9.8%
s 155405
8.9%
e 121747
6.9%
r 118926
6.8%
l 116103
6.6%
t 56154
 
3.2%
h 49637
 
2.8%
Other values (14) 285443
16.3%
Uppercase Letter
ValueCountFrequency (%)
C 41447
14.8%
M 33743
12.1%
N 30623
10.9%
I 27481
9.8%
T 21203
 
7.6%
O 15985
 
5.7%
F 15029
 
5.4%
A 11990
 
4.3%
W 10290
 
3.7%
Y 9712
 
3.5%
Other values (11) 62342
22.3%
Space Separator
ValueCountFrequency (%)
43363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2033273
97.9%
Common 43363
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 256726
12.6%
i 246073
12.1%
n 176038
 
8.7%
o 171176
 
8.4%
s 155405
 
7.6%
e 121747
 
6.0%
r 118926
 
5.8%
l 116103
 
5.7%
t 56154
 
2.8%
h 49637
 
2.4%
Other values (35) 565288
27.8%
Common
ValueCountFrequency (%)
43363
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2076636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 256726
12.4%
i 246073
11.8%
n 176038
 
8.5%
o 171176
 
8.2%
s 155405
 
7.5%
e 121747
 
5.9%
r 118926
 
5.7%
l 116103
 
5.6%
t 56154
 
2.7%
h 49637
 
2.4%
Other values (36) 608651
29.3%
Distinct12898
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:23.384046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length46
Median length43
Mean length9.3553658
Min length3

Characters and Unicode

Total characters2242266
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5031 ?
Unique (%)2.1%

Sample

1st rowMckeesport
2nd rowHawthorne
3rd rowLorain
4th rowAurora
5th rowGreensboro
ValueCountFrequency (%)
chicago 11796
 
3.8%
city 6413
 
2.1%
county 6348
 
2.0%
new 5148
 
1.7%
saint 4315
 
1.4%
baltimore 3966
 
1.3%
washington 3401
 
1.1%
san 3353
 
1.1%
orleans 3157
 
1.0%
beach 2994
 
1.0%
Other values (8671) 258802
83.6%
2023-11-25T22:19:23.915417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 200004
 
8.9%
o 181617
 
8.1%
e 181294
 
8.1%
n 168763
 
7.5%
i 152407
 
6.8%
l 137985
 
6.2%
t 123954
 
5.5%
r 117309
 
5.2%
s 98636
 
4.4%
70150
 
3.1%
Other values (49) 810147
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1842443
82.2%
Uppercase Letter 306110
 
13.7%
Space Separator 70150
 
3.1%
Close Punctuation 11462
 
0.5%
Open Punctuation 11462
 
0.5%
Dash Punctuation 512
 
< 0.1%
Other Punctuation 127
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 200004
10.9%
o 181617
9.9%
e 181294
9.8%
n 168763
9.2%
i 152407
 
8.3%
l 137985
 
7.5%
t 123954
 
6.7%
r 117309
 
6.4%
s 98636
 
5.4%
h 62169
 
3.4%
Other values (17) 418305
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 44556
14.6%
S 27436
 
9.0%
B 26681
 
8.7%
M 21634
 
7.1%
L 20173
 
6.6%
P 19538
 
6.4%
A 15128
 
4.9%
W 14652
 
4.8%
H 14198
 
4.6%
R 12615
 
4.1%
Other values (16) 89499
29.2%
Other Punctuation
ValueCountFrequency (%)
' 74
58.3%
. 53
41.7%
Space Separator
ValueCountFrequency (%)
70150
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11462
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11462
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2148553
95.8%
Common 93713
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 200004
 
9.3%
o 181617
 
8.5%
e 181294
 
8.4%
n 168763
 
7.9%
i 152407
 
7.1%
l 137985
 
6.4%
t 123954
 
5.8%
r 117309
 
5.5%
s 98636
 
4.6%
h 62169
 
2.9%
Other values (43) 724415
33.7%
Common
ValueCountFrequency (%)
70150
74.9%
) 11462
 
12.2%
( 11462
 
12.2%
- 512
 
0.5%
' 74
 
0.1%
. 53
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2242261
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 200004
 
8.9%
o 181617
 
8.1%
e 181294
 
8.1%
n 168763
 
7.5%
i 152407
 
6.8%
l 137985
 
6.2%
t 123954
 
5.5%
r 117309
 
5.2%
s 98636
 
4.4%
70150
 
3.1%
Other values (48) 810142
36.1%
None
ValueCountFrequency (%)
ñ 5
100.0%

address
Text

MISSING 

Distinct198037
Distinct (%)88.7%
Missing16497
Missing (%)6.9%
Memory size1.8 MiB
2023-11-25T22:19:24.291775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length97
Median length74
Mean length22.465454
Min length2

Characters and Unicode

Total characters5013840
Distinct characters93
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184863 ?
Unique (%)82.8%

Sample

1st row1506 Versailles Avenue and Coursin Street
2nd row13500 block of Cerise Avenue
3rd row1776 East 28th Street
4th row16000 block of East Ithaca Place
5th row307 Mourning Dove Terrace
ValueCountFrequency (%)
block 83046
 
8.7%
of 82994
 
8.7%
street 47009
 
4.9%
and 34826
 
3.6%
avenue 32127
 
3.4%
st 31940
 
3.3%
ave 21322
 
2.2%
road 20226
 
2.1%
drive 12957
 
1.4%
rd 12369
 
1.3%
Other values (39852) 577061
60.4%
2023-11-25T22:19:24.883384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
738367
 
14.7%
e 390118
 
7.8%
o 333685
 
6.7%
t 282064
 
5.6%
0 234554
 
4.7%
a 228926
 
4.6%
r 224218
 
4.5%
n 206961
 
4.1%
l 198571
 
4.0%
d 133862
 
2.7%
Other values (83) 2042514
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3039003
60.6%
Space Separator 738369
 
14.7%
Decimal Number 624340
 
12.5%
Uppercase Letter 589521
 
11.8%
Other Punctuation 19718
 
0.4%
Dash Punctuation 2721
 
0.1%
Final Punctuation 106
 
< 0.1%
Open Punctuation 21
 
< 0.1%
Close Punctuation 20
 
< 0.1%
Other Number 12
 
< 0.1%
Other values (5) 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 390118
12.8%
o 333685
11.0%
t 282064
 
9.3%
a 228926
 
7.5%
r 224218
 
7.4%
n 206961
 
6.8%
l 198571
 
6.5%
d 133862
 
4.4%
i 121302
 
4.0%
c 117620
 
3.9%
Other values (19) 801676
26.4%
Uppercase Letter
ValueCountFrequency (%)
S 125002
21.2%
A 67805
11.5%
R 45933
 
7.8%
W 36913
 
6.3%
C 34134
 
5.8%
B 32824
 
5.6%
D 30559
 
5.2%
N 29319
 
5.0%
E 27076
 
4.6%
M 23763
 
4.0%
Other values (16) 136193
23.1%
Decimal Number
ValueCountFrequency (%)
0 234554
37.6%
1 89287
 
14.3%
2 59061
 
9.5%
3 46263
 
7.4%
5 41835
 
6.7%
4 40154
 
6.4%
6 31171
 
5.0%
7 30002
 
4.8%
8 26838
 
4.3%
9 25175
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 18286
92.7%
, 582
 
3.0%
' 314
 
1.6%
/ 281
 
1.4%
# 124
 
0.6%
& 106
 
0.5%
; 18
 
0.1%
" 6
 
< 0.1%
\ 1
 
< 0.1%
Format
ValueCountFrequency (%)
 2
40.0%
1
20.0%
1
20.0%
­ 1
20.0%
Space Separator
ValueCountFrequency (%)
738367
> 99.9%
  2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2720
> 99.9%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 20
95.2%
[ 1
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 19
95.0%
] 1
 
5.0%
Other Number
ValueCountFrequency (%)
½ 11
91.7%
¼ 1
 
8.3%
Final Punctuation
ValueCountFrequency (%)
106
100.0%
Line Separator
ValueCountFrequency (%)
1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3628524
72.4%
Common 1385316
 
27.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 390118
 
10.8%
o 333685
 
9.2%
t 282064
 
7.8%
a 228926
 
6.3%
r 224218
 
6.2%
n 206961
 
5.7%
l 198571
 
5.5%
d 133862
 
3.7%
S 125002
 
3.4%
i 121302
 
3.3%
Other values (45) 1383815
38.1%
Common
ValueCountFrequency (%)
738367
53.3%
0 234554
 
16.9%
1 89287
 
6.4%
2 59061
 
4.3%
3 46263
 
3.3%
5 41835
 
3.0%
4 40154
 
2.9%
6 31171
 
2.3%
7 30002
 
2.2%
8 26838
 
1.9%
Other values (28) 47784
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5013583
> 99.9%
None 145
 
< 0.1%
Punctuation 111
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
738367
 
14.7%
e 390118
 
7.8%
o 333685
 
6.7%
t 282064
 
5.6%
0 234554
 
4.7%
a 228926
 
4.6%
r 224218
 
4.5%
n 206961
 
4.1%
l 198571
 
4.0%
d 133862
 
2.7%
Other values (68) 2042257
40.7%
None
ValueCountFrequency (%)
ñ 126
86.9%
½ 11
 
7.6%
  2
 
1.4%
 2
 
1.4%
¼ 1
 
0.7%
é 1
 
0.7%
­ 1
 
0.7%
í 1
 
0.7%
Punctuation
ValueCountFrequency (%)
106
95.5%
1
 
0.9%
1
 
0.9%
1
 
0.9%
1
 
0.9%
1
 
0.9%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

n_killed
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25228954
Minimum0
Maximum50
Zeros185835
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:25.064262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52177887
Coefficient of variation (CV)2.0681748
Kurtosis390.58532
Mean0.25228954
Median Absolute Deviation (MAD)0
Skewness6.636379
Sum60468
Variance0.27225319
MonotonicityNot monotonic
2023-11-25T22:19:25.224594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 185835
77.5%
1 48436
 
20.2%
2 4604
 
1.9%
3 595
 
0.2%
4 139
 
0.1%
5 41
 
< 0.1%
6 11
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
7 2
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
0 185835
77.5%
1 48436
 
20.2%
2 4604
 
1.9%
3 595
 
0.2%
4 139
 
0.1%
5 41
 
< 0.1%
6 11
 
< 0.1%
7 2
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
27 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 5
< 0.1%
7 2
 
< 0.1%
6 11
< 0.1%

n_injured
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49400652
Minimum0
Maximum53
Zeros142487
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:25.388219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum53
Range53
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72995227
Coefficient of variation (CV)1.4776167
Kurtosis142.82674
Mean0.49400652
Median Absolute Deviation (MAD)0
Skewness4.4433995
Sum118402
Variance0.53283032
MonotonicityNot monotonic
2023-11-25T22:19:25.556470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 142487
59.4%
1 81986
34.2%
2 11484
 
4.8%
3 2513
 
1.0%
4 760
 
0.3%
5 241
 
0.1%
6 91
 
< 0.1%
7 51
 
< 0.1%
8 19
 
< 0.1%
9 12
 
< 0.1%
Other values (13) 33
 
< 0.1%
ValueCountFrequency (%)
0 142487
59.4%
1 81986
34.2%
2 11484
 
4.8%
3 2513
 
1.0%
4 760
 
0.3%
5 241
 
0.1%
6 91
 
< 0.1%
7 51
 
< 0.1%
8 19
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
53 1
 
< 0.1%
25 1
 
< 0.1%
20 1
 
< 0.1%
19 3
< 0.1%
18 1
 
< 0.1%
17 2
< 0.1%
16 2
< 0.1%
15 2
< 0.1%
14 3
< 0.1%
13 2
< 0.1%

incident_url
Text

UNIQUE 

Distinct239677
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:25.942659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length50
Median length49
Mean length49.067921
Min length48

Characters and Unicode

Total characters11760452
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique239677 ?
Unique (%)100.0%

Sample

1st rowhttp://www.gunviolencearchive.org/incident/461105
2nd rowhttp://www.gunviolencearchive.org/incident/460726
3rd rowhttp://www.gunviolencearchive.org/incident/478855
4th rowhttp://www.gunviolencearchive.org/incident/478925
5th rowhttp://www.gunviolencearchive.org/incident/478959
ValueCountFrequency (%)
http://www.gunviolencearchive.org/incident/461105 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/479561 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/481198 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/481186 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/478855 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/478925 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/478959 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/478948 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/479363 1
 
< 0.1%
http://www.gunviolencearchive.org/incident/479374 1
 
< 0.1%
Other values (239667) 239667
> 99.9%
2023-11-25T22:19:26.531590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 958708
 
8.2%
/ 958708
 
8.2%
i 958708
 
8.2%
n 958708
 
8.2%
c 719031
 
6.1%
w 719031
 
6.1%
t 719031
 
6.1%
v 479354
 
4.1%
r 479354
 
4.1%
o 479354
 
4.1%
Other values (19) 4330465
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8628372
73.4%
Other Punctuation 1677739
 
14.3%
Decimal Number 1454341
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 958708
11.1%
i 958708
11.1%
n 958708
11.1%
c 719031
8.3%
w 719031
8.3%
t 719031
8.3%
v 479354
 
5.6%
r 479354
 
5.6%
o 479354
 
5.6%
h 479354
 
5.6%
Other values (6) 1677739
19.4%
Decimal Number
ValueCountFrequency (%)
1 175661
12.1%
3 144114
9.9%
4 143086
9.8%
9 142610
9.8%
2 142412
9.8%
7 141844
9.8%
8 141706
9.7%
5 141440
9.7%
6 141246
9.7%
0 140222
9.6%
Other Punctuation
ValueCountFrequency (%)
/ 958708
57.1%
. 479354
28.6%
: 239677
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 8628372
73.4%
Common 3132080
 
26.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 958708
11.1%
i 958708
11.1%
n 958708
11.1%
c 719031
8.3%
w 719031
8.3%
t 719031
8.3%
v 479354
 
5.6%
r 479354
 
5.6%
o 479354
 
5.6%
h 479354
 
5.6%
Other values (6) 1677739
19.4%
Common
ValueCountFrequency (%)
/ 958708
30.6%
. 479354
15.3%
: 239677
 
7.7%
1 175661
 
5.6%
3 144114
 
4.6%
4 143086
 
4.6%
9 142610
 
4.6%
2 142412
 
4.5%
7 141844
 
4.5%
8 141706
 
4.5%
Other values (3) 422908
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11760452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 958708
 
8.2%
/ 958708
 
8.2%
i 958708
 
8.2%
n 958708
 
8.2%
c 719031
 
6.1%
w 719031
 
6.1%
t 719031
 
6.1%
v 479354
 
4.1%
r 479354
 
4.1%
o 479354
 
4.1%
Other values (19) 4330465
36.8%
Distinct213989
Distinct (%)89.5%
Missing468
Missing (%)0.2%
Memory size1.8 MiB
2023-11-25T22:19:26.858045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length255
Median length225
Mean length94.044463
Min length2

Characters and Unicode

Total characters22496282
Distinct characters94
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique203257 ?
Unique (%)85.0%

Sample

1st rowhttp://www.post-gazette.com/local/south/2013/01/17/Man-arrested-in-New-Year-s-Eve-shooting-in-McKeesport/stories/201301170275
2nd rowhttp://www.dailybulletin.com/article/zz/20130105/NEWS/130109127
3rd rowhttp://chronicle.northcoastnow.com/2013/02/14/2-men-indicted-in-new-years-day-lorain-murder/
4th rowhttp://www.dailydemocrat.com/20130106/aurora-shootout-killer-was-frenetic-talented-neighbor-says
5th rowhttp://www.journalnow.com/news/local/article_d4c723e8-5a0f-11e2-a1fa-0019bb30f31a.html
ValueCountFrequency (%)
http://blog.tsa.gov 1179
 
0.5%
http://callsforservice.jaxsheriff.org 811
 
0.3%
https://data.oaklandnet.com/public-safety/crimewatch-maps-past-90-days/ym6k-rx7a 492
 
0.2%
http://itmdapps.ci.mil.wi.us/mpdcalldata/currentcadcalls/callsservice.faces 330
 
0.1%
http://www.springsgov.com/units/police/policeblotter.asp 243
 
0.1%
https://www.facebook.com/pg/policeclips/posts/?ref=page_internal 126
 
0.1%
http://www.tampabay.com/news/hillsborough/crime 107
 
< 0.1%
http://blog.tsa.gov/2016 104
 
< 0.1%
05 104
 
< 0.1%
http://blog.tsa.gov/search 87
 
< 0.1%
Other values (214167) 236416
98.5%
2023-11-25T22:19:27.388480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1546920
 
6.9%
- 1531611
 
6.8%
e 1493452
 
6.6%
/ 1449910
 
6.4%
o 1302203
 
5.8%
n 1132270
 
5.0%
s 1056971
 
4.7%
a 1016166
 
4.5%
i 1007666
 
4.5%
r 886137
 
3.9%
Other values (84) 10072976
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16120917
71.7%
Other Punctuation 2262946
 
10.1%
Decimal Number 2249761
 
10.0%
Dash Punctuation 1531611
 
6.8%
Uppercase Letter 169431
 
0.8%
Connector Punctuation 135869
 
0.6%
Math Symbol 24724
 
0.1%
Space Separator 868
 
< 0.1%
Open Punctuation 53
 
< 0.1%
Close Punctuation 52
 
< 0.1%
Other values (5) 50
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1546920
 
9.6%
e 1493452
 
9.3%
o 1302203
 
8.1%
n 1132270
 
7.0%
s 1056971
 
6.6%
a 1016166
 
6.3%
i 1007666
 
6.3%
r 886137
 
5.5%
w 877439
 
5.4%
c 847159
 
5.3%
Other values (16) 4954534
30.7%
Uppercase Letter
ValueCountFrequency (%)
S 19140
 
11.3%
P 14192
 
8.4%
C 12103
 
7.1%
N 11233
 
6.6%
D 10609
 
6.3%
M 10029
 
5.9%
W 9555
 
5.6%
A 9217
 
5.4%
E 8615
 
5.1%
F 6755
 
4.0%
Other values (16) 57983
34.2%
Other Punctuation
ValueCountFrequency (%)
/ 1449910
64.1%
. 532780
 
23.5%
: 240291
 
10.6%
# 12123
 
0.5%
? 9391
 
0.4%
& 8182
 
0.4%
% 6791
 
0.3%
, 2978
 
0.1%
! 341
 
< 0.1%
; 128
 
< 0.1%
Other values (5) 31
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 379735
16.9%
0 363273
16.1%
2 325058
14.4%
5 183678
8.2%
3 183337
8.1%
4 176774
7.9%
6 171599
7.6%
7 169095
7.5%
8 150051
 
6.7%
9 147161
 
6.5%
Math Symbol
ValueCountFrequency (%)
= 17395
70.4%
+ 7286
29.5%
~ 28
 
0.1%
| 15
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[ 41
77.4%
( 12
 
22.6%
Close Punctuation
ValueCountFrequency (%)
] 40
76.9%
) 12
 
23.1%
Modifier Symbol
ValueCountFrequency (%)
^ 9
69.2%
` 4
30.8%
Dash Punctuation
ValueCountFrequency (%)
- 1531611
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 135869
100.0%
Space Separator
ValueCountFrequency (%)
868
100.0%
Final Punctuation
ValueCountFrequency (%)
18
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 11
100.0%
Control
ValueCountFrequency (%)
7
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16290348
72.4%
Common 6205934
 
27.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1546920
 
9.5%
e 1493452
 
9.2%
o 1302203
 
8.0%
n 1132270
 
7.0%
s 1056971
 
6.5%
a 1016166
 
6.2%
i 1007666
 
6.2%
r 886137
 
5.4%
w 877439
 
5.4%
c 847159
 
5.2%
Other values (42) 5123965
31.5%
Common
ValueCountFrequency (%)
- 1531611
24.7%
/ 1449910
23.4%
. 532780
 
8.6%
1 379735
 
6.1%
0 363273
 
5.9%
2 325058
 
5.2%
: 240291
 
3.9%
5 183678
 
3.0%
3 183337
 
3.0%
4 176774
 
2.8%
Other values (32) 839487
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22496261
> 99.9%
Punctuation 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1546920
 
6.9%
- 1531611
 
6.8%
e 1493452
 
6.6%
/ 1449910
 
6.4%
o 1302203
 
5.8%
n 1132270
 
5.0%
s 1056971
 
4.7%
a 1016166
 
4.5%
i 1007666
 
4.5%
r 886137
 
3.9%
Other values (81) 10072955
44.8%
Punctuation
ValueCountFrequency (%)
18
85.7%
2
 
9.5%
1
 
4.8%

incident_url_fields_missing
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.2 KiB
False
239677 
ValueCountFrequency (%)
False 239677
100.0%
2023-11-25T22:19:27.536720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

congressional_district
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)< 0.1%
Missing11944
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean8.0012646
Minimum0
Maximum53
Zeros421
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:27.694803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile26
Maximum53
Range53
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.4808348
Coefficient of variation (CV)1.0599368
Kurtosis5.169602
Mean8.0012646
Median Absolute Deviation (MAD)3
Skewness2.1063618
Sum1822152
Variance71.924559
MonotonicityNot monotonic
2023-11-25T22:19:27.906930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 36910
15.4%
2 26945
11.2%
3 20621
 
8.6%
7 19709
 
8.2%
4 18469
 
7.7%
5 16512
 
6.9%
9 9867
 
4.1%
6 9388
 
3.9%
8 7353
 
3.1%
13 6175
 
2.6%
Other values (44) 55784
23.3%
(Missing) 11944
 
5.0%
ValueCountFrequency (%)
0 421
 
0.2%
1 36910
15.4%
2 26945
11.2%
3 20621
8.6%
4 18469
7.7%
5 16512
6.9%
6 9388
 
3.9%
7 19709
8.2%
8 7353
 
3.1%
9 9867
 
4.1%
ValueCountFrequency (%)
53 130
 
0.1%
52 158
 
0.1%
51 228
0.1%
50 116
 
< 0.1%
49 100
 
< 0.1%
48 119
 
< 0.1%
47 292
0.1%
46 251
0.1%
45 45
 
< 0.1%
44 421
0.2%

gun_stolen
Text

MISSING 

Distinct349
Distinct (%)0.2%
Missing99498
Missing (%)41.5%
Memory size1.8 MiB
2023-11-25T22:19:28.122221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5488
Median length10
Mean length14.563365
Min length8

Characters and Unicode

Total characters2041478
Distinct characters24
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)0.1%

Sample

1st row0::Unknown||1::Unknown
2nd row0::Unknown||1::Unknown
3rd row0::Unknown||1::Unknown
4th row0::Unknown
5th row0::Unknown
ValueCountFrequency (%)
0::unknown 121310
86.5%
0::unknown||1::unknown 6116
 
4.4%
0::stolen 4503
 
3.2%
0::unknown||1::unknown||2::unknown 1484
 
1.1%
0::not-stolen 1352
 
1.0%
0::stolen||1::stolen 719
 
0.5%
0::unknown||1::unknown||2::unknown||3::unknown 583
 
0.4%
0::stolen||1::unknown 375
 
0.3%
0:unknown 336
 
0.2%
0::stolen||1::stolen||2::stolen 294
 
0.2%
Other values (339) 3107
 
2.2%
2023-11-25T22:19:28.560644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 538167
26.4%
: 384282
18.8%
o 194149
 
9.5%
U 172912
 
8.5%
k 172912
 
8.5%
w 172912
 
8.5%
0 143437
 
7.0%
| 104279
 
5.1%
1 25555
 
1.3%
t 21237
 
1.0%
Other values (14) 111636
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1140045
55.8%
Other Punctuation 384282
 
18.8%
Decimal Number 218723
 
10.7%
Uppercase Letter 192343
 
9.4%
Math Symbol 104279
 
5.1%
Dash Punctuation 1806
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143437
65.6%
1 25555
 
11.7%
2 13189
 
6.0%
3 8672
 
4.0%
4 6170
 
2.8%
5 5115
 
2.3%
6 4688
 
2.1%
7 4256
 
1.9%
8 3942
 
1.8%
9 3699
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
n 538167
47.2%
o 194149
 
17.0%
k 172912
 
15.2%
w 172912
 
15.2%
t 21237
 
1.9%
l 19431
 
1.7%
e 19431
 
1.7%
s 1806
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U 172912
89.9%
S 17625
 
9.2%
N 1806
 
0.9%
Other Punctuation
ValueCountFrequency (%)
: 384282
100.0%
Math Symbol
ValueCountFrequency (%)
| 104279
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1806
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1332388
65.3%
Common 709090
34.7%

Most frequent character per script

Common
ValueCountFrequency (%)
: 384282
54.2%
0 143437
 
20.2%
| 104279
 
14.7%
1 25555
 
3.6%
2 13189
 
1.9%
3 8672
 
1.2%
4 6170
 
0.9%
5 5115
 
0.7%
6 4688
 
0.7%
7 4256
 
0.6%
Other values (3) 9447
 
1.3%
Latin
ValueCountFrequency (%)
n 538167
40.4%
o 194149
 
14.6%
U 172912
 
13.0%
k 172912
 
13.0%
w 172912
 
13.0%
t 21237
 
1.6%
l 19431
 
1.5%
e 19431
 
1.5%
S 17625
 
1.3%
N 1806
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2041478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 538167
26.4%
: 384282
18.8%
o 194149
 
9.5%
U 172912
 
8.5%
k 172912
 
8.5%
w 172912
 
8.5%
0 143437
 
7.0%
| 104279
 
5.1%
1 25555
 
1.3%
t 21237
 
1.0%
Other values (14) 111636
 
5.5%

gun_type
Text

MISSING 

Distinct2502
Distinct (%)1.8%
Missing99451
Missing (%)41.5%
Memory size1.8 MiB
2023-11-25T22:19:28.770751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5488
Median length10
Mean length14.433736
Min length5

Characters and Unicode

Total characters2023985
Distinct characters44
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1867 ?
Unique (%)1.3%

Sample

1st row0::Unknown||1::Unknown
2nd row0::Handgun||1::Handgun
3rd row0::22 LR||1::223 Rem [AR-15]
4th row0::Unknown
5th row0::Unknown
ValueCountFrequency (%)
0::unknown 93559
58.1%
0::handgun 13018
 
8.1%
auto 4773
 
3.0%
0::9mm 4599
 
2.9%
0::22 2602
 
1.6%
lr 2558
 
1.6%
0::unknown||1::unknown 2410
 
1.5%
0::40 2240
 
1.4%
sw 2217
 
1.4%
0::shotgun 2151
 
1.3%
Other values (2722) 30842
 
19.2%
2023-11-25T22:19:29.206666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 448059
22.1%
: 384499
19.0%
0 149516
 
7.4%
o 141293
 
7.0%
U 131154
 
6.5%
k 131154
 
6.5%
w 131154
 
6.5%
| 104403
 
5.2%
u 36648
 
1.8%
g 33261
 
1.6%
Other values (34) 332844
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1035627
51.2%
Other Punctuation 385439
 
19.0%
Decimal Number 275165
 
13.6%
Uppercase Letter 194755
 
9.6%
Math Symbol 104403
 
5.2%
Space Separator 20743
 
1.0%
Dash Punctuation 2747
 
0.1%
Open Punctuation 2553
 
0.1%
Close Punctuation 2553
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 448059
43.3%
o 141293
 
13.6%
k 131154
 
12.7%
w 131154
 
12.7%
u 36648
 
3.5%
g 33261
 
3.2%
a 27531
 
2.7%
d 25050
 
2.4%
m 14635
 
1.4%
t 11204
 
1.1%
Other values (7) 35638
 
3.4%
Decimal Number
ValueCountFrequency (%)
0 149516
54.3%
1 28495
 
10.4%
2 26517
 
9.6%
3 16236
 
5.9%
4 12728
 
4.6%
5 10539
 
3.8%
9 10164
 
3.7%
8 8254
 
3.0%
7 6965
 
2.5%
6 5751
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
U 131154
67.3%
H 25050
 
12.9%
R 11855
 
6.1%
S 8915
 
4.6%
A 8421
 
4.3%
L 3358
 
1.7%
W 2975
 
1.5%
O 1065
 
0.5%
M 1022
 
0.5%
K 940
 
0.5%
Other Punctuation
ValueCountFrequency (%)
: 384499
99.8%
. 940
 
0.2%
Math Symbol
ValueCountFrequency (%)
| 104403
100.0%
Space Separator
ValueCountFrequency (%)
20743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2747
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2553
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1230382
60.8%
Common 793603
39.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 448059
36.4%
o 141293
 
11.5%
U 131154
 
10.7%
k 131154
 
10.7%
w 131154
 
10.7%
u 36648
 
3.0%
g 33261
 
2.7%
a 27531
 
2.2%
d 25050
 
2.0%
H 25050
 
2.0%
Other values (17) 100028
 
8.1%
Common
ValueCountFrequency (%)
: 384499
48.4%
0 149516
 
18.8%
| 104403
 
13.2%
1 28495
 
3.6%
2 26517
 
3.3%
20743
 
2.6%
3 16236
 
2.0%
4 12728
 
1.6%
5 10539
 
1.3%
9 10164
 
1.3%
Other values (7) 29763
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2023985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 448059
22.1%
: 384499
19.0%
0 149516
 
7.4%
o 141293
 
7.0%
U 131154
 
6.5%
k 131154
 
6.5%
w 131154
 
6.5%
| 104403
 
5.2%
u 36648
 
1.8%
g 33261
 
1.6%
Other values (34) 332844
16.4%
Distinct18126
Distinct (%)7.6%
Missing326
Missing (%)0.1%
Memory size1.8 MiB
2023-11-25T22:19:29.412496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length761
Median length628
Mean length82.218098
Min length8

Characters and Unicode

Total characters19678984
Distinct characters64
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12168 ?
Unique (%)5.1%

Sample

1st rowShot - Wounded/Injured||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person
2nd rowShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Gang involvement
3rd rowShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Shots Fired - No Injuries||Bar/club incident - in or around establishment
4th rowShot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Drug involvement||Kidnapping/abductions/hostage||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )
5th rowShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Attempted Murder/Suicide (one variable unsuccessful)||Domestic Violence
ValueCountFrequency (%)
244816
 
11.3%
shot 141035
 
6.5%
of 73576
 
3.4%
no 59089
 
2.7%
accidental 53868
 
2.5%
murder 53507
 
2.5%
dead 53409
 
2.5%
wounded/injured 47541
 
2.2%
involved 44737
 
2.1%
found 43214
 
2.0%
Other values (2764) 1353615
62.4%
2023-11-25T22:19:29.869223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1929056
 
9.8%
e 1580812
 
8.0%
o 1482316
 
7.5%
n 1462911
 
7.4%
i 1204643
 
6.1%
d 1058725
 
5.4%
r 1049310
 
5.3%
t 964476
 
4.9%
s 900485
 
4.6%
u 735368
 
3.7%
Other values (54) 7310882
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14347747
72.9%
Space Separator 1929056
 
9.8%
Uppercase Letter 1551514
 
7.9%
Math Symbol 679388
 
3.5%
Other Punctuation 520467
 
2.6%
Dash Punctuation 318941
 
1.6%
Open Punctuation 158292
 
0.8%
Close Punctuation 158292
 
0.8%
Decimal Number 9943
 
0.1%
Modifier Symbol 5344
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1580812
11.0%
o 1482316
10.3%
n 1462911
10.2%
i 1204643
 
8.4%
d 1058725
 
7.4%
r 1049310
 
7.3%
t 964476
 
6.7%
s 900485
 
6.3%
u 735368
 
5.1%
c 661520
 
4.6%
Other values (15) 3247181
22.6%
Uppercase Letter
ValueCountFrequency (%)
S 301883
19.5%
I 296569
19.1%
D 142763
9.2%
N 106602
 
6.9%
A 102855
 
6.6%
W 101843
 
6.6%
P 60098
 
3.9%
F 53741
 
3.5%
O 50254
 
3.2%
G 49950
 
3.2%
Other values (12) 284956
18.4%
Other Punctuation
ValueCountFrequency (%)
/ 378906
72.8%
, 139476
 
26.8%
: 1478
 
0.3%
& 607
 
0.1%
Decimal Number
ValueCountFrequency (%)
4 3787
38.1%
1 2052
20.6%
5 2052
20.6%
7 2052
20.6%
Math Symbol
ValueCountFrequency (%)
| 677653
99.7%
+ 1735
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 150732
95.2%
{ 7560
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 150732
95.2%
} 7560
 
4.8%
Space Separator
ValueCountFrequency (%)
1929056
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 318941
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 5344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15899261
80.8%
Common 3779723
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1580812
 
9.9%
o 1482316
 
9.3%
n 1462911
 
9.2%
i 1204643
 
7.6%
d 1058725
 
6.7%
r 1049310
 
6.6%
t 964476
 
6.1%
s 900485
 
5.7%
u 735368
 
4.6%
c 661520
 
4.2%
Other values (37) 4798695
30.2%
Common
ValueCountFrequency (%)
1929056
51.0%
| 677653
 
17.9%
/ 378906
 
10.0%
- 318941
 
8.4%
( 150732
 
4.0%
) 150732
 
4.0%
, 139476
 
3.7%
{ 7560
 
0.2%
} 7560
 
0.2%
^ 5344
 
0.1%
Other values (7) 13763
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19678984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1929056
 
9.8%
e 1580812
 
8.0%
o 1482316
 
7.5%
n 1462911
 
7.4%
i 1204643
 
6.1%
d 1058725
 
5.4%
r 1049310
 
5.3%
t 964476
 
4.9%
s 900485
 
4.6%
u 735368
 
3.7%
Other values (54) 7310882
37.2%

latitude
Real number (ℝ)

MISSING 

Distinct101240
Distinct (%)43.7%
Missing7923
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean37.546598
Minimum19.1114
Maximum71.3368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:30.082662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum19.1114
5-th percentile29.2205
Q133.9034
median38.5706
Q341.437375
95-th percentile44.0544
Maximum71.3368
Range52.2254
Interquartile range (IQR)7.533975

Descriptive statistics

Standard deviation5.1307632
Coefficient of variation (CV)0.13665055
Kurtosis1.8789007
Mean37.546598
Median Absolute Deviation (MAD)3.3471
Skewness0.2072284
Sum8701574.3
Variance26.324731
MonotonicityNot monotonic
2023-11-25T22:19:30.298701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.6356 253
 
0.1%
39.294 244
 
0.1%
29.9872 170
 
0.1%
33.4347 161
 
0.1%
32.8982 160
 
0.1%
38.9075 142
 
0.1%
36.1334 112
 
< 0.1%
29.9551 109
 
< 0.1%
28.436 109
 
< 0.1%
39.8496 108
 
< 0.1%
Other values (101230) 230186
96.0%
(Missing) 7923
 
3.3%
ValueCountFrequency (%)
19.1114 1
< 0.1%
19.1127 1
< 0.1%
19.2 1
< 0.1%
19.2017 1
< 0.1%
19.4243 1
< 0.1%
19.4331 1
< 0.1%
19.4475 1
< 0.1%
19.4554 1
< 0.1%
19.4578 1
< 0.1%
19.4581 1
< 0.1%
ValueCountFrequency (%)
71.3368 1
< 0.1%
71.3005 1
< 0.1%
71.3001 1
< 0.1%
71.3 1
< 0.1%
71.2997 1
< 0.1%
71.2921 1
< 0.1%
71.2906 1
< 0.1%
70.6698 1
< 0.1%
70.1981 1
< 0.1%
67.5533 1
< 0.1%

location_description
Text

MISSING 

Distinct27595
Distinct (%)65.6%
Missing197588
Missing (%)82.4%
Memory size1.8 MiB
2023-11-25T22:19:30.620318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length100
Median length54
Mean length17.352657
Min length1

Characters and Unicode

Total characters730356
Distinct characters94
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23867 ?
Unique (%)56.7%

Sample

1st rowCotton Club
2nd rowFairmont Terrace
3rd rowNite Owl Tavern
4th rowClub Venue
5th rowNew Castle County courthouse
ValueCountFrequency (%)
apartments 4271
 
3.9%
park 3361
 
3.1%
school 1831
 
1.7%
neighborhood 1407
 
1.3%
high 1196
 
1.1%
airport 1070
 
1.0%
inn 1006
 
0.9%
and 937
 
0.9%
bar 858
 
0.8%
club 848
 
0.8%
Other values (14701) 92848
84.7%
2023-11-25T22:19:31.189058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67550
 
9.2%
e 60163
 
8.2%
a 54551
 
7.5%
r 48460
 
6.6%
o 48250
 
6.6%
t 44529
 
6.1%
n 44105
 
6.0%
i 35853
 
4.9%
l 33487
 
4.6%
s 30873
 
4.2%
Other values (84) 262535
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 541059
74.1%
Uppercase Letter 110848
 
15.2%
Space Separator 67560
 
9.3%
Other Punctuation 4528
 
0.6%
Decimal Number 2666
 
0.4%
Open Punctuation 1039
 
0.1%
Close Punctuation 1037
 
0.1%
Dash Punctuation 1035
 
0.1%
Final Punctuation 563
 
0.1%
Initial Punctuation 8
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60163
11.1%
a 54551
10.1%
r 48460
 
9.0%
o 48250
 
8.9%
t 44529
 
8.2%
n 44105
 
8.2%
i 35853
 
6.6%
l 33487
 
6.2%
s 30873
 
5.7%
h 17981
 
3.3%
Other values (21) 122807
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 11831
 
10.7%
C 11000
 
9.9%
A 10048
 
9.1%
P 8802
 
7.9%
M 7686
 
6.9%
H 6667
 
6.0%
B 6057
 
5.5%
L 4856
 
4.4%
T 4765
 
4.3%
G 4479
 
4.0%
Other values (16) 34657
31.3%
Other Punctuation
ValueCountFrequency (%)
' 2685
59.3%
& 754
 
16.7%
. 535
 
11.8%
/ 364
 
8.0%
, 136
 
3.0%
" 30
 
0.7%
# 17
 
0.4%
@ 3
 
0.1%
! 2
 
< 0.1%
; 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 465
17.4%
7 427
16.0%
6 372
14.0%
2 280
10.5%
0 254
9.5%
8 190
7.1%
4 180
 
6.8%
5 176
 
6.6%
3 170
 
6.4%
9 152
 
5.7%
Space Separator
ValueCountFrequency (%)
67550
> 99.9%
8
 
< 0.1%
  2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1036
99.7%
[ 3
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 1034
99.7%
] 3
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1029
99.4%
6
 
0.6%
Final Punctuation
ValueCountFrequency (%)
562
99.8%
1
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Modifier Symbol
ValueCountFrequency (%)
` 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 651907
89.3%
Common 78449
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60163
 
9.2%
a 54551
 
8.4%
r 48460
 
7.4%
o 48250
 
7.4%
t 44529
 
6.8%
n 44105
 
6.8%
i 35853
 
5.5%
l 33487
 
5.1%
s 30873
 
4.7%
h 17981
 
2.8%
Other values (47) 233655
35.8%
Common
ValueCountFrequency (%)
67550
86.1%
' 2685
 
3.4%
( 1036
 
1.3%
) 1034
 
1.3%
- 1029
 
1.3%
& 754
 
1.0%
562
 
0.7%
. 535
 
0.7%
1 465
 
0.6%
7 427
 
0.5%
Other values (27) 2372
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729751
99.9%
Punctuation 585
 
0.1%
None 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67550
 
9.3%
e 60163
 
8.2%
a 54551
 
7.5%
r 48460
 
6.6%
o 48250
 
6.6%
t 44529
 
6.1%
n 44105
 
6.0%
i 35853
 
4.9%
l 33487
 
4.6%
s 30873
 
4.2%
Other values (72) 261930
35.9%
Punctuation
ValueCountFrequency (%)
562
96.1%
8
 
1.4%
7
 
1.2%
6
 
1.0%
1
 
0.2%
1
 
0.2%
None
ValueCountFrequency (%)
é 12
60.0%
  2
 
10.0%
ñ 2
 
10.0%
à 2
 
10.0%
á 1
 
5.0%
ó 1
 
5.0%

longitude
Real number (ℝ)

MISSING 

Distinct112347
Distinct (%)48.5%
Missing7923
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean-89.338348
Minimum-171.429
Maximum97.4331
Zeros0
Zeros (%)0.0%
Negative231749
Negative (%)96.7%
Memory size1.8 MiB
2023-11-25T22:19:31.582816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-171.429
5-th percentile-121.41635
Q1-94.158725
median-86.2496
Q3-80.048625
95-th percentile-73.228765
Maximum97.4331
Range268.8621
Interquartile range (IQR)14.1101

Descriptive statistics

Standard deviation14.359546
Coefficient of variation (CV)-0.16073216
Kurtosis2.5309184
Mean-89.338348
Median Absolute Deviation (MAD)6.83845
Skewness-1.3548337
Sum-20704520
Variance206.19655
MonotonicityNot monotonic
2023-11-25T22:19:31.780169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-84.4333 254
 
0.1%
-76.62 234
 
0.1%
-112.006 173
 
0.1%
-95.3477 169
 
0.1%
-97.0404 160
 
0.1%
-77.0176 135
 
0.1%
-122.296 119
 
< 0.1%
-104.674 113
 
< 0.1%
-90.0747 112
 
< 0.1%
-81.3065 106
 
< 0.1%
Other values (112337) 230179
96.0%
(Missing) 7923
 
3.3%
ValueCountFrequency (%)
-171.429 1
< 0.1%
-166.541 1
< 0.1%
-166.097 1
< 0.1%
-165.711 2
< 0.1%
-165.586 1
< 0.1%
-165.509 1
< 0.1%
-165.444 1
< 0.1%
-164.76 1
< 0.1%
-164.621 1
< 0.1%
-164.62 1
< 0.1%
ValueCountFrequency (%)
97.4331 2
< 0.1%
96.7591 1
< 0.1%
90.37 1
< 0.1%
80.9491 1
< 0.1%
-67.2711 1
< 0.1%
-67.275 1
< 0.1%
-67.2996 1
< 0.1%
-67.3318 1
< 0.1%
-67.4012 1
< 0.1%
-67.7617 1
< 0.1%

n_guns_involved
Real number (ℝ)

MISSING  SKEWED 

Distinct106
Distinct (%)0.1%
Missing99451
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean1.3724416
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:32.009465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum400
Range399
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6782022
Coefficient of variation (CV)3.4086712
Kurtosis3574.4227
Mean1.3724416
Median Absolute Deviation (MAD)0
Skewness51.595073
Sum192452
Variance21.885576
MonotonicityNot monotonic
2023-11-25T22:19:32.231189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 127548
53.2%
2 7477
 
3.1%
3 2021
 
0.8%
4 871
 
0.4%
5 435
 
0.2%
6 285
 
0.1%
7 232
 
0.1%
8 137
 
0.1%
9 111
 
< 0.1%
10 103
 
< 0.1%
Other values (96) 1006
 
0.4%
(Missing) 99451
41.5%
ValueCountFrequency (%)
1 127548
53.2%
2 7477
 
3.1%
3 2021
 
0.8%
4 871
 
0.4%
5 435
 
0.2%
6 285
 
0.1%
7 232
 
0.1%
8 137
 
0.1%
9 111
 
< 0.1%
10 103
 
< 0.1%
ValueCountFrequency (%)
400 4
< 0.1%
399 1
 
< 0.1%
374 1
 
< 0.1%
346 1
 
< 0.1%
338 1
 
< 0.1%
323 1
 
< 0.1%
300 3
< 0.1%
280 1
 
< 0.1%
276 1
 
< 0.1%
268 1
 
< 0.1%

notes
Text

MISSING 

Distinct136652
Distinct (%)86.1%
Missing81017
Missing (%)33.8%
Memory size1.8 MiB
2023-11-25T22:19:32.591835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length256
Median length209
Mean length56.068272
Min length1

Characters and Unicode

Total characters8895792
Distinct characters113
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132570 ?
Unique (%)83.6%

Sample

1st rowJulian Sims under investigation: Four Shot and Injured
2nd rowFour Shot; One Killed; Unidentified shooter in getaway car
3rd rowTwo firearms recovered. (Attempted) murder suicide - both succeeded in fulfilling an M/S and did not succeed, based on details.
4th rowUnprovoked drive-by results in multiple teens and young adults injured. No source with names or exact ages.
5th rowPerps were likely motivated by gang affliations
ValueCountFrequency (%)
shot 51224
 
3.5%
in 46684
 
3.2%
man 25376
 
1.7%
at 24903
 
1.7%
and 24760
 
1.7%
of 18355
 
1.2%
to 17653
 
1.2%
fired 17268
 
1.2%
gun 17081
 
1.2%
with 16267
 
1.1%
Other values (60021) 1222138
82.5%
2023-11-25T22:19:33.166874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1318260
14.8%
e 699024
 
7.9%
o 539193
 
6.1%
t 528838
 
5.9%
n 526584
 
5.9%
a 487633
 
5.5%
i 476111
 
5.4%
r 457335
 
5.1%
s 439499
 
4.9%
d 317870
 
3.6%
Other values (103) 3105445
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6589180
74.1%
Space Separator 1318265
 
14.8%
Decimal Number 329600
 
3.7%
Other Punctuation 315674
 
3.5%
Uppercase Letter 267473
 
3.0%
Control 36565
 
0.4%
Dash Punctuation 34611
 
0.4%
Close Punctuation 1470
 
< 0.1%
Open Punctuation 1469
 
< 0.1%
Final Punctuation 718
 
< 0.1%
Other values (8) 767
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 699024
 
10.6%
o 539193
 
8.2%
t 528838
 
8.0%
n 526584
 
8.0%
a 487633
 
7.4%
i 476111
 
7.2%
r 457335
 
6.9%
s 439499
 
6.7%
d 317870
 
4.8%
h 288625
 
4.4%
Other values (19) 1828468
27.7%
Uppercase Letter
ValueCountFrequency (%)
S 30463
 
11.4%
M 26064
 
9.7%
A 21984
 
8.2%
C 21369
 
8.0%
P 18338
 
6.9%
D 13668
 
5.1%
B 12989
 
4.9%
T 12066
 
4.5%
R 11687
 
4.4%
H 11011
 
4.1%
Other values (17) 87834
32.8%
Other Punctuation
ValueCountFrequency (%)
, 136807
43.3%
. 80920
25.6%
; 61479
19.5%
/ 16066
 
5.1%
: 7601
 
2.4%
' 7516
 
2.4%
& 2416
 
0.8%
" 1562
 
0.5%
# 601
 
0.2%
* 368
 
0.1%
Other values (5) 338
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 54168
16.4%
2 46098
14.0%
3 36140
11.0%
0 36123
11.0%
4 30328
9.2%
8 26860
8.1%
5 26670
8.1%
9 26217
8.0%
7 25028
7.6%
6 21968
6.7%
Math Symbol
ValueCountFrequency (%)
~ 213
62.8%
+ 82
 
24.2%
= 39
 
11.5%
| 2
 
0.6%
> 2
 
0.6%
< 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1318260
> 99.9%
  3
 
< 0.1%
2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 34597
> 99.9%
12
 
< 0.1%
2
 
< 0.1%
Control
ValueCountFrequency (%)
29048
79.4%
7514
 
20.5%
3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1132
77.0%
] 337
 
22.9%
} 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1130
76.9%
[ 339
 
23.1%
Final Punctuation
ValueCountFrequency (%)
643
89.6%
75
 
10.4%
Initial Punctuation
ValueCountFrequency (%)
82
83.7%
16
 
16.3%
Other Symbol
ValueCountFrequency (%)
° 9
81.8%
® 2
 
18.2%
Format
ValueCountFrequency (%)
1
50.0%
1
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 264
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 50
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6856653
77.1%
Common 2039139
 
22.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1318260
64.6%
, 136807
 
6.7%
. 80920
 
4.0%
; 61479
 
3.0%
1 54168
 
2.7%
2 46098
 
2.3%
3 36140
 
1.8%
0 36123
 
1.8%
- 34597
 
1.7%
4 30328
 
1.5%
Other values (47) 204219
 
10.0%
Latin
ValueCountFrequency (%)
e 699024
 
10.2%
o 539193
 
7.9%
t 528838
 
7.7%
n 526584
 
7.7%
a 487633
 
7.1%
i 476111
 
6.9%
r 457335
 
6.7%
s 439499
 
6.4%
d 317870
 
4.6%
h 288625
 
4.2%
Other values (46) 2095941
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8894918
> 99.9%
Punctuation 834
 
< 0.1%
None 39
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1318260
14.8%
e 699024
 
7.9%
o 539193
 
6.1%
t 528838
 
5.9%
n 526584
 
5.9%
a 487633
 
5.5%
i 476111
 
5.4%
r 457335
 
5.1%
s 439499
 
4.9%
d 317870
 
3.6%
Other values (85) 3104571
34.9%
Punctuation
ValueCountFrequency (%)
643
77.1%
82
 
9.8%
75
 
9.0%
16
 
1.9%
12
 
1.4%
2
 
0.2%
2
 
0.2%
1
 
0.1%
1
 
0.1%
None
ValueCountFrequency (%)
é 13
33.3%
° 9
23.1%
ñ 8
20.5%
  3
 
7.7%
® 2
 
5.1%
½ 2
 
5.1%
Ñ 1
 
2.6%
ú 1
 
2.6%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

participant_age
Text

MISSING 

Distinct18951
Distinct (%)12.9%
Missing92298
Missing (%)38.5%
Memory size1.8 MiB
2023-11-25T22:19:33.471111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length517
Median length5
Mean length8.481534
Min length3

Characters and Unicode

Total characters1250000
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13962 ?
Unique (%)9.5%

Sample

1st row0::20
2nd row0::20
3rd row0::25||1::31||2::33||3::34||4::33
4th row0::29||1::33||2::56||3::33
5th row0::18||1::46||2::14||3::47
ValueCountFrequency (%)
0::24 3814
 
2.6%
0::23 3735
 
2.5%
0::22 3733
 
2.5%
0::19 3719
 
2.5%
0::21 3612
 
2.5%
0::18 3536
 
2.4%
0::20 3535
 
2.4%
0::25 3500
 
2.4%
0::26 3277
 
2.2%
0::27 3110
 
2.1%
Other values (18941) 111808
75.9%
2023-11-25T22:19:34.005565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 440049
35.2%
0 155438
 
12.4%
| 147354
 
11.8%
2 129127
 
10.3%
1 124587
 
10.0%
3 71865
 
5.7%
4 44217
 
3.5%
5 34423
 
2.8%
6 27255
 
2.2%
8 25721
 
2.1%
Other values (2) 49964
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 662597
53.0%
Other Punctuation 440049
35.2%
Math Symbol 147354
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155438
23.5%
2 129127
19.5%
1 124587
18.8%
3 71865
10.8%
4 44217
 
6.7%
5 34423
 
5.2%
6 27255
 
4.1%
8 25721
 
3.9%
7 25634
 
3.9%
9 24330
 
3.7%
Other Punctuation
ValueCountFrequency (%)
: 440049
100.0%
Math Symbol
ValueCountFrequency (%)
| 147354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1250000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 440049
35.2%
0 155438
 
12.4%
| 147354
 
11.8%
2 129127
 
10.3%
1 124587
 
10.0%
3 71865
 
5.7%
4 44217
 
3.5%
5 34423
 
2.8%
6 27255
 
2.2%
8 25721
 
2.1%
Other values (2) 49964
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 440049
35.2%
0 155438
 
12.4%
| 147354
 
11.8%
2 129127
 
10.3%
1 124587
 
10.0%
3 71865
 
5.7%
4 44217
 
3.5%
5 34423
 
2.8%
6 27255
 
2.2%
8 25721
 
2.1%
Other values (2) 49964
 
4.0%

participant_age_group
Text

MISSING 

Distinct898
Distinct (%)0.5%
Missing42119
Missing (%)17.6%
Memory size1.8 MiB
2023-11-25T22:19:34.307036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1536
Median length12
Mean length21.963312
Min length11

Characters and Unicode

Total characters4339028
Distinct characters26
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique488 ?
Unique (%)0.2%

Sample

1st row0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+
2nd row0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+
3rd row0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+
4th row0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+
5th row0::Adult 18+||1::Adult 18+||2::Teen 12-17||3::Adult 18+
ValueCountFrequency (%)
18 181166
33.9%
0::adult 172425
32.3%
18+||1::adult 73182
13.7%
18+||2::adult 25269
 
4.7%
12-17 14623
 
2.7%
0::teen 13169
 
2.5%
18+||3::adult 9699
 
1.8%
1::adult 5320
 
1.0%
18+||4::adult 3776
 
0.7%
18+||1::teen 3415
 
0.6%
Other values (307) 32132
 
6.0%
2023-11-25T22:19:34.858923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 666899
15.4%
1 459224
10.6%
336618
7.8%
d 311003
7.2%
l 311003
7.2%
8 306769
7.1%
A 306435
7.1%
u 306435
7.1%
t 306435
7.1%
+ 306435
7.1%
Other values (16) 721772
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1320857
30.4%
Decimal Number 1066457
24.6%
Other Punctuation 666899
15.4%
Math Symbol 581396
13.4%
Space Separator 336618
 
7.8%
Uppercase Letter 336618
 
7.8%
Dash Punctuation 30183
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 459224
43.1%
8 306769
28.8%
0 195930
18.4%
2 57349
 
5.4%
7 26175
 
2.5%
3 12574
 
1.2%
4 5075
 
0.5%
5 2124
 
0.2%
6 1007
 
0.1%
9 230
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 311003
23.5%
l 311003
23.5%
u 306435
23.2%
t 306435
23.2%
e 51230
 
3.9%
n 25615
 
1.9%
h 4568
 
0.3%
i 4568
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
A 306435
91.0%
T 25615
 
7.6%
C 4568
 
1.4%
Math Symbol
ValueCountFrequency (%)
+ 306435
52.7%
| 274961
47.3%
Other Punctuation
ValueCountFrequency (%)
: 666899
100.0%
Space Separator
ValueCountFrequency (%)
336618
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2681553
61.8%
Latin 1657475
38.2%

Most frequent character per script

Common
ValueCountFrequency (%)
: 666899
24.9%
1 459224
17.1%
336618
12.6%
8 306769
11.4%
+ 306435
11.4%
| 274961
10.3%
0 195930
 
7.3%
2 57349
 
2.1%
- 30183
 
1.1%
7 26175
 
1.0%
Other values (5) 21010
 
0.8%
Latin
ValueCountFrequency (%)
d 311003
18.8%
l 311003
18.8%
A 306435
18.5%
u 306435
18.5%
t 306435
18.5%
e 51230
 
3.1%
T 25615
 
1.5%
n 25615
 
1.5%
C 4568
 
0.3%
h 4568
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4339028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 666899
15.4%
1 459224
10.6%
336618
7.8%
d 311003
7.2%
l 311003
7.2%
8 306769
7.1%
A 306435
7.1%
u 306435
7.1%
t 306435
7.1%
+ 306435
7.1%
Other values (16) 721772
16.6%

participant_gender
Text

MISSING 

Distinct873
Distinct (%)0.4%
Missing36362
Missing (%)15.2%
Memory size1.8 MiB
2023-11-25T22:19:35.088862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length803
Median length624
Mean length13.980124
Min length6

Characters and Unicode

Total characters2842369
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique486 ?
Unique (%)0.2%

Sample

1st row0::Male||1::Male||3::Male||4::Female
2nd row0::Male
3rd row0::Male||1::Male||2::Male||3::Male||4::Male
4th row0::Female||1::Male||2::Male||3::Male
5th row0::Female||1::Male||2::Male||3::Female
ValueCountFrequency (%)
0::male 93497
46.0%
0::male||1::male 43530
21.4%
0::male||1::male||2::male 12383
 
6.1%
0::female||1::male 10602
 
5.2%
0::female 7791
 
3.8%
0::male||1::female 5130
 
2.5%
0::male||1::male||2::male||3::male 4333
 
2.1%
1::male 4168
 
2.1%
0::female||1::male||2::male 2062
 
1.0%
0::male||1::female||2::male 1819
 
0.9%
Other values (863) 18001
 
8.9%
2023-11-25T22:19:35.574604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 698742
24.6%
e 395437
13.9%
a 352264
12.4%
l 352264
12.4%
M 309091
10.9%
| 295226
10.4%
0 196029
 
6.9%
1 99390
 
3.5%
m 43173
 
1.5%
F 43172
 
1.5%
Other values (11) 57581
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1143139
40.2%
Other Punctuation 698743
24.6%
Decimal Number 352997
 
12.4%
Uppercase Letter 352263
 
12.4%
Math Symbol 295226
 
10.4%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196029
55.5%
1 99390
28.2%
2 34472
 
9.8%
3 13564
 
3.8%
4 5322
 
1.5%
5 2187
 
0.6%
6 991
 
0.3%
7 529
 
0.1%
8 306
 
0.1%
9 207
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 395437
34.6%
a 352264
30.8%
l 352264
30.8%
m 43173
 
3.8%
f 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 698742
> 99.9%
, 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 309091
87.7%
F 43172
 
12.3%
Math Symbol
ValueCountFrequency (%)
| 295226
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1495402
52.6%
Common 1346967
47.4%

Most frequent character per script

Common
ValueCountFrequency (%)
: 698742
51.9%
| 295226
21.9%
0 196029
 
14.6%
1 99390
 
7.4%
2 34472
 
2.6%
3 13564
 
1.0%
4 5322
 
0.4%
5 2187
 
0.2%
6 991
 
0.1%
7 529
 
< 0.1%
Other values (4) 515
 
< 0.1%
Latin
ValueCountFrequency (%)
e 395437
26.4%
a 352264
23.6%
l 352264
23.6%
M 309091
20.7%
m 43173
 
2.9%
F 43172
 
2.9%
f 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2842369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 698742
24.6%
e 395437
13.9%
a 352264
12.4%
l 352264
12.4%
M 309091
10.9%
| 295226
10.4%
0 196029
 
6.9%
1 99390
 
3.5%
m 43173
 
1.5%
F 43172
 
1.5%
Other values (11) 57581
 
2.0%

participant_name
Text

MISSING 

Distinct113488
Distinct (%)96.6%
Missing122253
Missing (%)51.0%
Memory size1.8 MiB
2023-11-25T22:19:35.993907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2152
Median length554
Mean length29.761914
Min length4

Characters and Unicode

Total characters3494763
Distinct characters94
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110585 ?
Unique (%)94.2%

Sample

1st row0::Julian Sims
2nd row0::Bernard Gillis
3rd row0::Damien Bell||1::Desmen Noble||2::Herman Seagers||3::Ladd Tate Sr||4::Tallis Moore
4th row0::Stacie Philbrook||1::Christopher Ratliffe||2::Anthony Ticali||3::Sonny Archuleta
5th row0::Danielle Imani Jameison||1::Maurice Eugene Edmonds, Sr.||2::Maurice Edmonds II||3::Sandra Palmer
ValueCountFrequency (%)
jr 4278
 
1.2%
0::michael 2062
 
0.6%
lee 1982
 
0.5%
l 1829
 
0.5%
d 1812
 
0.5%
williams 1812
 
0.5%
johnson 1652
 
0.5%
smith 1628
 
0.4%
a 1616
 
0.4%
j 1589
 
0.4%
Other values (119259) 346483
94.5%
2023-11-25T22:19:36.630230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 358855
 
10.3%
e 259679
 
7.4%
251008
 
7.2%
a 244437
 
7.0%
n 202010
 
5.8%
r 198777
 
5.7%
o 169272
 
4.8%
i 158096
 
4.5%
l 137607
 
3.9%
| 125788
 
3.6%
Other values (84) 1389234
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2109817
60.4%
Uppercase Letter 442476
 
12.7%
Other Punctuation 379235
 
10.9%
Space Separator 251009
 
7.2%
Decimal Number 181719
 
5.2%
Math Symbol 125788
 
3.6%
Dash Punctuation 3414
 
0.1%
Final Punctuation 776
 
< 0.1%
Initial Punctuation 398
 
< 0.1%
Open Punctuation 57
 
< 0.1%
Other values (5) 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 259679
12.3%
a 244437
11.6%
n 202010
9.6%
r 198777
9.4%
o 169272
 
8.0%
i 158096
 
7.5%
l 137607
 
6.5%
s 115005
 
5.5%
t 93988
 
4.5%
h 78578
 
3.7%
Other values (19) 452368
21.4%
Uppercase Letter
ValueCountFrequency (%)
J 48266
 
10.9%
M 37653
 
8.5%
D 35687
 
8.1%
C 31678
 
7.2%
S 29761
 
6.7%
A 28194
 
6.4%
R 27952
 
6.3%
B 25651
 
5.8%
T 22354
 
5.1%
L 22258
 
5.0%
Other values (17) 133022
30.1%
Decimal Number
ValueCountFrequency (%)
0 99308
54.6%
1 52934
29.1%
2 17136
 
9.4%
3 6825
 
3.8%
4 2849
 
1.6%
5 1232
 
0.7%
6 618
 
0.3%
7 366
 
0.2%
8 239
 
0.1%
9 212
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 358855
94.6%
. 15062
 
4.0%
" 2789
 
0.7%
, 1363
 
0.4%
' 1088
 
0.3%
/ 69
 
< 0.1%
* 5
 
< 0.1%
& 3
 
< 0.1%
1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3410
99.9%
3
 
0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
251008
> 99.9%
  1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
392
50.5%
384
49.5%
Initial Punctuation
ValueCountFrequency (%)
387
97.2%
11
 
2.8%
Open Punctuation
ValueCountFrequency (%)
( 55
96.5%
[ 2
 
3.5%
Close Punctuation
ValueCountFrequency (%)
) 55
96.5%
] 2
 
3.5%
Format
ValueCountFrequency (%)
­ 6
85.7%
 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
| 125788
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2552293
73.0%
Common 942470
 
27.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 259679
 
10.2%
a 244437
 
9.6%
n 202010
 
7.9%
r 198777
 
7.8%
o 169272
 
6.6%
i 158096
 
6.2%
l 137607
 
5.4%
s 115005
 
4.5%
t 93988
 
3.7%
h 78578
 
3.1%
Other values (46) 894844
35.1%
Common
ValueCountFrequency (%)
: 358855
38.1%
251008
26.6%
| 125788
 
13.3%
0 99308
 
10.5%
1 52934
 
5.6%
2 17136
 
1.8%
. 15062
 
1.6%
3 6825
 
0.7%
- 3410
 
0.4%
4 2849
 
0.3%
Other values (28) 9295
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3493562
> 99.9%
Punctuation 1179
 
< 0.1%
None 22
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 358855
 
10.3%
e 259679
 
7.4%
251008
 
7.2%
a 244437
 
7.0%
n 202010
 
5.8%
r 198777
 
5.7%
o 169272
 
4.8%
i 158096
 
4.5%
l 137607
 
3.9%
| 125788
 
3.6%
Other values (70) 1388033
39.7%
Punctuation
ValueCountFrequency (%)
392
33.2%
387
32.8%
384
32.6%
11
 
0.9%
3
 
0.3%
1
 
0.1%
1
 
0.1%
None
ValueCountFrequency (%)
ñ 11
50.0%
­ 6
27.3%
 1
 
4.5%
ç 1
 
4.5%
  1
 
4.5%
Á 1
 
4.5%
ó 1
 
4.5%
Distinct284
Distinct (%)1.8%
Missing223903
Missing (%)93.4%
Memory size1.8 MiB
2023-11-25T22:19:36.879725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length366
Median length274
Mean length27.692976
Min length8

Characters and Unicode

Total characters436829
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.8%

Sample

1st row3::Family
2nd row5::Family
3rd row4::Drive by - Random victims||5::Drive by - Random victims||6::Drive by - Random victims
4th row7::Aquaintance
5th row5::Gang vs Gang||6::Gang vs Gang
ValueCountFrequency (%)
5442
 
9.9%
robbery 4704
 
8.6%
1::armed 3521
 
6.4%
others 3471
 
6.3%
current 3471
 
6.3%
or 3471
 
6.3%
former 3456
 
6.3%
1::significant 2658
 
4.8%
1::family 2573
 
4.7%
perp 1912
 
3.5%
Other values (216) 20167
36.8%
2023-11-25T22:19:37.371017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 41054
 
9.4%
r 40823
 
9.3%
39072
 
8.9%
e 34562
 
7.9%
o 27960
 
6.4%
i 22723
 
5.2%
n 20544
 
4.7%
m 18927
 
4.3%
b 16711
 
3.8%
t 14804
 
3.4%
Other values (41) 159649
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 281438
64.4%
Other Punctuation 41054
 
9.4%
Uppercase Letter 39564
 
9.1%
Space Separator 39072
 
8.9%
Decimal Number 20574
 
4.7%
Math Symbol 9544
 
2.2%
Dash Punctuation 5583
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 40823
14.5%
e 34562
12.3%
o 27960
9.9%
i 22723
 
8.1%
n 20544
 
7.3%
m 18927
 
6.7%
b 16711
 
5.9%
t 14804
 
5.3%
a 12257
 
4.4%
y 11573
 
4.1%
Other values (13) 60554
21.5%
Uppercase Letter
ValueCountFrequency (%)
A 8989
22.7%
R 8028
20.3%
F 4650
11.8%
S 3471
 
8.8%
N 2101
 
5.3%
P 1912
 
4.8%
K 1912
 
4.8%
V 1912
 
4.8%
I 1899
 
4.8%
H 1899
 
4.8%
Other values (4) 2791
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 12266
59.6%
2 3768
 
18.3%
0 2291
 
11.1%
3 1432
 
7.0%
4 520
 
2.5%
5 176
 
0.9%
6 63
 
0.3%
7 29
 
0.1%
8 18
 
0.1%
9 11
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 41054
100.0%
Space Separator
ValueCountFrequency (%)
39072
100.0%
Math Symbol
ValueCountFrequency (%)
| 9544
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 321002
73.5%
Common 115827
 
26.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 40823
12.7%
e 34562
 
10.8%
o 27960
 
8.7%
i 22723
 
7.1%
n 20544
 
6.4%
m 18927
 
5.9%
b 16711
 
5.2%
t 14804
 
4.6%
a 12257
 
3.8%
y 11573
 
3.6%
Other values (27) 100118
31.2%
Common
ValueCountFrequency (%)
: 41054
35.4%
39072
33.7%
1 12266
 
10.6%
| 9544
 
8.2%
- 5583
 
4.8%
2 3768
 
3.3%
0 2291
 
2.0%
3 1432
 
1.2%
4 520
 
0.4%
5 176
 
0.2%
Other values (4) 121
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 436829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 41054
 
9.4%
r 40823
 
9.3%
39072
 
8.9%
e 34562
 
7.9%
o 27960
 
6.4%
i 22723
 
5.2%
n 20544
 
4.7%
m 18927
 
4.3%
b 16711
 
3.8%
t 14804
 
3.4%
Other values (41) 159649
36.5%

participant_status
Text

MISSING 

Distinct2150
Distinct (%)1.0%
Missing27626
Missing (%)11.5%
Memory size1.8 MiB
2023-11-25T22:19:37.585164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1500
Median length746
Mean length24.29358
Min length8

Characters and Unicode

Total characters5151478
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1181 ?
Unique (%)0.6%

Sample

1st row0::Arrested||1::Injured||2::Injured||3::Injured||4::Injured
2nd row0::Killed||1::Injured||2::Injured||3::Injured
3rd row0::Injured, Unharmed, Arrested||1::Unharmed, Arrested||2::Killed||3::Injured||4::Injured
4th row0::Killed||1::Killed||2::Killed||3::Killed
5th row0::Injured||1::Injured||2::Killed||3::Killed
ValueCountFrequency (%)
arrested 62404
20.7%
0::unharmed 46059
15.3%
0::injured 44095
14.6%
0::injured||1::unharmed 22189
 
7.4%
0::killed 21265
 
7.1%
0::killed||1::unharmed 14657
 
4.9%
0::unharmed||1::unharmed 9826
 
3.3%
arrested||1::unharmed 8755
 
2.9%
arrested||2::unharmed 7947
 
2.6%
0::injured||1::injured 6473
 
2.1%
Other values (1177) 57636
19.1%
2023-11-25T22:19:38.044323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 752047
14.6%
e 567922
 
11.0%
r 507444
 
9.9%
d 468589
 
9.1%
| 331209
 
6.4%
n 308778
 
6.0%
0 208732
 
4.1%
U 190383
 
3.7%
h 190383
 
3.7%
a 190383
 
3.7%
Other values (21) 1435608
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3040772
59.0%
Other Punctuation 841302
 
16.3%
Uppercase Letter 468589
 
9.1%
Decimal Number 380351
 
7.4%
Math Symbol 331209
 
6.4%
Space Separator 89255
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 567922
18.7%
r 507444
16.7%
d 468589
15.4%
n 308778
10.2%
h 190383
 
6.3%
a 190383
 
6.3%
m 190383
 
6.3%
l 120956
 
4.0%
u 118395
 
3.9%
j 118395
 
3.9%
Other values (3) 259144
8.5%
Decimal Number
ValueCountFrequency (%)
0 208732
54.9%
1 106989
28.1%
2 38041
 
10.0%
3 15281
 
4.0%
4 6156
 
1.6%
5 2578
 
0.7%
6 1220
 
0.3%
7 673
 
0.2%
8 397
 
0.1%
9 284
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U 190383
40.6%
I 118395
25.3%
A 99333
21.2%
K 60478
 
12.9%
Other Punctuation
ValueCountFrequency (%)
: 752047
89.4%
, 89255
 
10.6%
Math Symbol
ValueCountFrequency (%)
| 331209
100.0%
Space Separator
ValueCountFrequency (%)
89255
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3509361
68.1%
Common 1642117
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 567922
16.2%
r 507444
14.5%
d 468589
13.4%
n 308778
8.8%
U 190383
 
5.4%
h 190383
 
5.4%
a 190383
 
5.4%
m 190383
 
5.4%
l 120956
 
3.4%
u 118395
 
3.4%
Other values (7) 655745
18.7%
Common
ValueCountFrequency (%)
: 752047
45.8%
| 331209
20.2%
0 208732
 
12.7%
1 106989
 
6.5%
89255
 
5.4%
, 89255
 
5.4%
2 38041
 
2.3%
3 15281
 
0.9%
4 6156
 
0.4%
5 2578
 
0.2%
Other values (4) 2574
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5151478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 752047
14.6%
e 567922
 
11.0%
r 507444
 
9.9%
d 468589
 
9.1%
| 331209
 
6.4%
n 308778
 
6.0%
0 208732
 
4.1%
U 190383
 
3.7%
h 190383
 
3.7%
a 190383
 
3.7%
Other values (21) 1435608
27.9%

participant_type
Text

MISSING 

Distinct259
Distinct (%)0.1%
Missing24863
Missing (%)10.4%
Memory size1.8 MiB
2023-11-25T22:19:38.263800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1311
Median length1236
Mean length26.395221
Min length8

Characters and Unicode

Total characters5670063
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)< 0.1%

Sample

1st row0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspect
2nd row0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspect
3rd row0::Subject-Suspect||1::Subject-Suspect||2::Victim||3::Victim||4::Victim
4th row0::Victim||1::Victim||2::Victim||3::Subject-Suspect
5th row0::Victim||1::Victim||2::Victim||3::Subject-Suspect
ValueCountFrequency (%)
0::victim 58564
27.3%
0::victim||1::subject-suspect 50579
23.5%
0::subject-suspect 44914
20.9%
0::victim||1::subject-suspect||2::subject-suspect 10941
 
5.1%
0::victim||1::victim 9033
 
4.2%
0::subject-suspect||1::subject-suspect 8922
 
4.2%
0::victim||1::victim||2::subject-suspect 6552
 
3.1%
0::victim||1::subject-suspect||2::subject-suspect||3::subject-suspect 3720
 
1.7%
0::subject-suspect||1::subject-suspect||2::subject-suspect 3040
 
1.4%
0::victim||1::victim||2::subject-suspect||3::subject-suspect 2107
 
1.0%
Other values (249) 16442
 
7.7%
2023-11-25T22:19:38.705065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 777836
13.7%
c 591586
10.4%
t 591586
10.4%
S 398526
 
7.0%
u 398526
 
7.0%
e 398526
 
7.0%
i 386120
 
6.8%
| 351532
 
6.2%
0 215020
 
3.8%
p 199263
 
3.5%
Other values (15) 1361542
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3356456
59.2%
Other Punctuation 777836
 
13.7%
Uppercase Letter 591586
 
10.4%
Decimal Number 393390
 
6.9%
Math Symbol 351532
 
6.2%
Dash Punctuation 199263
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 591586
17.6%
t 591586
17.6%
u 398526
11.9%
e 398526
11.9%
i 386120
11.5%
p 199263
 
5.9%
s 199263
 
5.9%
j 199263
 
5.9%
b 199263
 
5.9%
m 193060
 
5.8%
Decimal Number
ValueCountFrequency (%)
0 215020
54.7%
1 110860
28.2%
2 39613
 
10.1%
3 15996
 
4.1%
4 6478
 
1.6%
5 2721
 
0.7%
6 1287
 
0.3%
7 702
 
0.2%
8 417
 
0.1%
9 296
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
S 398526
67.4%
V 193060
32.6%
Other Punctuation
ValueCountFrequency (%)
: 777836
100.0%
Math Symbol
ValueCountFrequency (%)
| 351532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 199263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3948042
69.6%
Common 1722021
30.4%

Most frequent character per script

Common
ValueCountFrequency (%)
: 777836
45.2%
| 351532
20.4%
0 215020
 
12.5%
- 199263
 
11.6%
1 110860
 
6.4%
2 39613
 
2.3%
3 15996
 
0.9%
4 6478
 
0.4%
5 2721
 
0.2%
6 1287
 
0.1%
Other values (3) 1415
 
0.1%
Latin
ValueCountFrequency (%)
c 591586
15.0%
t 591586
15.0%
S 398526
10.1%
u 398526
10.1%
e 398526
10.1%
i 386120
9.8%
p 199263
 
5.0%
s 199263
 
5.0%
j 199263
 
5.0%
b 199263
 
5.0%
Other values (2) 386120
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5670063
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 777836
13.7%
c 591586
10.4%
t 591586
10.4%
S 398526
 
7.0%
u 398526
 
7.0%
e 398526
 
7.0%
i 386120
 
6.8%
| 351532
 
6.2%
0 215020
 
3.8%
p 199263
 
3.5%
Other values (15) 1361542
24.0%
Distinct217280
Distinct (%)90.9%
Missing609
Missing (%)0.3%
Memory size1.8 MiB
2023-11-25T22:19:39.052208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3015
Median length1227
Mean length132.79768
Min length8

Characters and Unicode

Total characters31747675
Distinct characters86
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208428 ?
Unique (%)87.2%

Sample

1st rowhttp://pittsburgh.cbslocal.com/2013/01/01/4-people-shot-in-mckeesport/||http://www.wtae.com/news/local/allegheny/U-S-Marshals-task-force-arrests-New-Year-s-party-shooting-suspect/17977588||http://www.post-gazette.com/local/south/2013/01/17/Man-arrested-in-New-Year-s-Eve-shooting-in-McKeesport/stories/201301170275
2nd rowhttp://losangeles.cbslocal.com/2013/01/01/man-killed-3-wounded-at-nye-party-in-hawthorne/||http://latimesblogs.latimes.com/lanow/2013/01/hawthorne-new-year-party-three-killed.html||https://usgunviolence.wordpress.com/2013/01/01/killed-man-hawthorne-ca/||http://www.dailybulletin.com/article/zz/20130105/NEWS/130109127
3rd rowhttp://www.morningjournal.com/general-news/20130222/lorain-man-pleads-innocent-to-new-years-murder||http://chronicle.northcoastnow.com/2013/02/14/2-men-indicted-in-new-years-day-lorain-murder/
4th rowhttp://denver.cbslocal.com/2013/01/06/officer-told-neighbor-standoff-gunman-was-on-meth-binge/||http://www.westword.com/news/sonny-archuleta-triple-murder-in-aurora-guns-purchased-legally-55-57-5900504||http://www.denverpost.com/ci_22322380/aurora-shooter-was-frenetic-talented-neighbor-says||http://www.dailymail.co.uk/news/article-2258008/Sonny-Archuleta-Gunman-left-dead-latest-Aurora-shooting-lost-brother-gun-violence.html||http://www.dailydemocrat.com/20130106/aurora-shootout-killer-was-frenetic-talented-neighbor-says
5th rowhttp://myfox8.com/2013/01/08/update-mother-shot-14-year-old-son-two-others-before-killing-herself/||http://myfox8.com/2013/01/07/police-respond-to-report-of-triple-shooting-in-greensboro/||http://www.journalnow.com/news/local/article_d4c723e8-5a0f-11e2-a1fa-0019bb30f31a.html
ValueCountFrequency (%)
http://blog.tsa.gov 1179
 
0.5%
http://callsforservice.jaxsheriff.org 802
 
0.3%
https://data.oaklandnet.com/public-safety/crimewatch-maps-past-90-days/ym6k-rx7a 491
 
0.2%
http://itmdapps.ci.mil.wi.us/mpdcalldata/currentcadcalls/callsservice.faces 327
 
0.1%
http://www.springsgov.com/units/police/policeblotter.asp 224
 
0.1%
https://www.facebook.com/pg/policeclips/posts/?ref=page_internal 124
 
0.1%
http://www.tampabay.com/news/hillsborough/crime 107
 
< 0.1%
http://blog.tsa.gov/search 87
 
< 0.1%
https://www.montereysheriff.org/mcsologs/dpl.pdf 84
 
< 0.1%
https://www.springsgov.com/units/police/policeblotter.asp 78
 
< 0.1%
Other values (217262) 235565
98.5%
2023-11-25T22:19:39.614283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2184150
 
6.9%
- 2176370
 
6.9%
e 2083789
 
6.6%
/ 2030216
 
6.4%
o 1860015
 
5.9%
n 1609263
 
5.1%
s 1479276
 
4.7%
i 1433344
 
4.5%
a 1415467
 
4.5%
w 1243029
 
3.9%
Other values (76) 14232756
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22655559
71.4%
Other Punctuation 3158871
 
9.9%
Decimal Number 3114108
 
9.8%
Dash Punctuation 2176370
 
6.9%
Uppercase Letter 236225
 
0.7%
Math Symbol 225530
 
0.7%
Connector Punctuation 180859
 
0.6%
Open Punctuation 71
 
< 0.1%
Close Punctuation 69
 
< 0.1%
Currency Symbol 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2184150
 
9.6%
e 2083789
 
9.2%
o 1860015
 
8.2%
n 1609263
 
7.1%
s 1479276
 
6.5%
i 1433344
 
6.3%
a 1415467
 
6.2%
w 1243029
 
5.5%
r 1216178
 
5.4%
c 1182658
 
5.2%
Other values (16) 6948390
30.7%
Uppercase Letter
ValueCountFrequency (%)
S 27243
 
11.5%
P 18898
 
8.0%
C 16146
 
6.8%
N 15212
 
6.4%
M 13967
 
5.9%
D 13875
 
5.9%
A 12833
 
5.4%
W 12621
 
5.3%
E 11282
 
4.8%
F 9850
 
4.2%
Other values (16) 84298
35.7%
Other Punctuation
ValueCountFrequency (%)
/ 2030216
64.3%
. 738511
 
23.4%
: 335577
 
10.6%
# 16191
 
0.5%
? 13325
 
0.4%
& 11922
 
0.4%
% 9048
 
0.3%
, 3402
 
0.1%
! 434
 
< 0.1%
; 209
 
< 0.1%
Other values (3) 36
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 523990
16.8%
0 492305
15.8%
2 448660
14.4%
3 262415
8.4%
5 254938
8.2%
4 242476
7.8%
6 238398
7.7%
7 236397
7.6%
8 209882
6.7%
9 204647
 
6.6%
Math Symbol
ValueCountFrequency (%)
| 189629
84.1%
= 25185
 
11.2%
+ 10677
 
4.7%
~ 39
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 54
76.1%
( 17
 
23.9%
Close Punctuation
ValueCountFrequency (%)
] 52
75.4%
) 17
 
24.6%
Dash Punctuation
ValueCountFrequency (%)
- 2176370
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 180859
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22891784
72.1%
Common 8855891
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2184150
 
9.5%
e 2083789
 
9.1%
o 1860015
 
8.1%
n 1609263
 
7.0%
s 1479276
 
6.5%
i 1433344
 
6.3%
a 1415467
 
6.2%
w 1243029
 
5.4%
r 1216178
 
5.3%
c 1182658
 
5.2%
Other values (42) 7184615
31.4%
Common
ValueCountFrequency (%)
- 2176370
24.6%
/ 2030216
22.9%
. 738511
 
8.3%
1 523990
 
5.9%
0 492305
 
5.6%
2 448660
 
5.1%
: 335577
 
3.8%
3 262415
 
3.0%
5 254938
 
2.9%
4 242476
 
2.7%
Other values (24) 1350433
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31747675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2184150
 
6.9%
- 2176370
 
6.9%
e 2083789
 
6.6%
/ 2030216
 
6.4%
o 1860015
 
5.9%
n 1609263
 
5.1%
s 1479276
 
4.7%
i 1433344
 
4.5%
a 1415467
 
4.5%
w 1243029
 
3.9%
Other values (76) 14232756
44.8%

state_house_district
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)0.1%
Missing38772
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean55.447132
Minimum1
Maximum901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:39.809652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q121
median47
Q384
95-th percentile134
Maximum901
Range900
Interquartile range (IQR)63

Descriptive statistics

Standard deviation42.048117
Coefficient of variation (CV)0.75834611
Kurtosis17.431432
Mean55.447132
Median Absolute Deviation (MAD)30
Skewness1.8224792
Sum11139606
Variance1768.0441
MonotonicityNot monotonic
2023-11-25T22:19:40.001539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3476
 
1.5%
10 3411
 
1.4%
32 3218
 
1.3%
31 3192
 
1.3%
3 3064
 
1.3%
2 2941
 
1.2%
34 2897
 
1.2%
6 2870
 
1.2%
13 2762
 
1.2%
29 2680
 
1.1%
Other values (265) 170394
71.1%
(Missing) 38772
 
16.2%
ValueCountFrequency (%)
1 2056
0.9%
2 2941
1.2%
3 3064
1.3%
4 1928
0.8%
5 2603
1.1%
6 2870
1.2%
7 2059
0.9%
8 2475
1.0%
9 2492
1.0%
10 3411
1.4%
ValueCountFrequency (%)
901 1
< 0.1%
814 1
< 0.1%
813 1
< 0.1%
811 1
< 0.1%
809 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%
804 1
< 0.1%
801 1
< 0.1%
729 2
< 0.1%

state_senate_district
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)< 0.1%
Missing32335
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean20.47711
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-11-25T22:19:40.190848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median19
Q330
95-th percentile46
Maximum94
Range93
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.20456
Coefficient of variation (CV)0.69367989
Kurtosis-0.24424374
Mean20.47711
Median Absolute Deviation (MAD)11
Skewness0.63840614
Sum4245765
Variance201.76951
MonotonicityNot monotonic
2023-11-25T22:19:40.390636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10041
 
4.2%
9 7963
 
3.3%
3 7837
 
3.3%
4 7328
 
3.1%
2 6666
 
2.8%
15 5835
 
2.4%
6 5817
 
2.4%
19 5644
 
2.4%
14 5641
 
2.4%
11 5620
 
2.3%
Other values (58) 138950
58.0%
(Missing) 32335
 
13.5%
ValueCountFrequency (%)
1 5206
2.2%
2 6666
2.8%
3 7837
3.3%
4 7328
3.1%
5 10041
4.2%
6 5817
2.4%
7 4742
2.0%
8 3962
 
1.7%
9 7963
3.3%
10 4542
1.9%
ValueCountFrequency (%)
94 1
 
< 0.1%
67 69
 
< 0.1%
66 33
 
< 0.1%
65 124
 
0.1%
64 20
 
< 0.1%
63 867
0.4%
62 350
0.1%
61 177
 
0.1%
60 204
 
0.1%
59 487
0.2%

Interactions

2023-11-25T22:19:15.820073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:05.625448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.841770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.042856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.256130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.535283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.782683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.208140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.527768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.963711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:05.770243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.971791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.173405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.392973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.671495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.930774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.355213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.669826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.109571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:05.903123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.105248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.308184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.532373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.809946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.073535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.497559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.815230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.282457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.035266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.238080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.445757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.670607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.954171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.218534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.644404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.957603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.437004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.169920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.365664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.579651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.830480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.091955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.363717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.791530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.103569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.578591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.308664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.505440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.718519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.985064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.233124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.509095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.939765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.250120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.712574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.443059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.637248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.842398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.113351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.362503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.796645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.080681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.380489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:16.874321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.584487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.781524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:08.989147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.263621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.513551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:12.940790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.237078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.537295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:17.016484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:06.713328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:07.909965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:09.121572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:10.396017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:11.646803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:13.077309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:14.375746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T22:19:15.673385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-25T22:19:40.524913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
congressional_districtincident_idlatitudelongituden_guns_involvedn_injuredn_killedstate_house_districtstate_senate_district
congressional_district1.0000.006-0.167-0.033-0.001-0.0090.0430.2310.343
incident_id0.0061.0000.023-0.021-0.0620.0170.007-0.020-0.009
latitude-0.1670.0231.0000.1520.0290.000-0.100-0.264-0.016
longitude-0.033-0.0210.1521.0000.0120.025-0.0720.1540.092
n_guns_involved-0.001-0.0620.0290.0121.000-0.119-0.084-0.0000.007
n_injured-0.0090.0170.0000.025-0.1191.000-0.256-0.040-0.058
n_killed0.0430.007-0.100-0.072-0.084-0.2561.000-0.001-0.003
state_house_district0.231-0.020-0.2640.154-0.000-0.040-0.0011.0000.508
state_senate_district0.343-0.009-0.0160.0920.007-0.058-0.0030.5081.000

Missing values

2023-11-25T22:19:17.405725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-25T22:19:18.389166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-25T22:19:20.505966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

incident_iddatestatecity_or_countyaddressn_killedn_injuredincident_urlsource_urlincident_url_fields_missingcongressional_districtgun_stolengun_typeincident_characteristicslatitudelocation_descriptionlongituden_guns_involvednotesparticipant_ageparticipant_age_groupparticipant_genderparticipant_nameparticipant_relationshipparticipant_statusparticipant_typesourcesstate_house_districtstate_senate_district
04611052013-01-01PennsylvaniaMckeesport1506 Versailles Avenue and Coursin Street04http://www.gunviolencearchive.org/incident/461105http://www.post-gazette.com/local/south/2013/01/17/Man-arrested-in-New-Year-s-Eve-shooting-in-McKeesport/stories/201301170275False14.0NaNNaNShot - Wounded/Injured||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person40.3467NaN-79.8559NaNJulian Sims under investigation: Four Shot and Injured0::200::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+0::Male||1::Male||3::Male||4::Female0::Julian SimsNaN0::Arrested||1::Injured||2::Injured||3::Injured||4::Injured0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspecthttp://pittsburgh.cbslocal.com/2013/01/01/4-people-shot-in-mckeesport/||http://www.wtae.com/news/local/allegheny/U-S-Marshals-task-force-arrests-New-Year-s-party-shooting-suspect/17977588||http://www.post-gazette.com/local/south/2013/01/17/Man-arrested-in-New-Year-s-Eve-shooting-in-McKeesport/stories/201301170275NaNNaN
14607262013-01-01CaliforniaHawthorne13500 block of Cerise Avenue13http://www.gunviolencearchive.org/incident/460726http://www.dailybulletin.com/article/zz/20130105/NEWS/130109127False43.0NaNNaNShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Gang involvement33.9090NaN-118.3330NaNFour Shot; One Killed; Unidentified shooter in getaway car0::200::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+0::Male0::Bernard GillisNaN0::Killed||1::Injured||2::Injured||3::Injured0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspecthttp://losangeles.cbslocal.com/2013/01/01/man-killed-3-wounded-at-nye-party-in-hawthorne/||http://latimesblogs.latimes.com/lanow/2013/01/hawthorne-new-year-party-three-killed.html||https://usgunviolence.wordpress.com/2013/01/01/killed-man-hawthorne-ca/||http://www.dailybulletin.com/article/zz/20130105/NEWS/13010912762.035.0
24788552013-01-01OhioLorain1776 East 28th Street13http://www.gunviolencearchive.org/incident/478855http://chronicle.northcoastnow.com/2013/02/14/2-men-indicted-in-new-years-day-lorain-murder/False9.00::Unknown||1::Unknown0::Unknown||1::UnknownShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Shots Fired - No Injuries||Bar/club incident - in or around establishment41.4455Cotton Club-82.13772.0NaN0::25||1::31||2::33||3::34||4::330::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+0::Male||1::Male||2::Male||3::Male||4::Male0::Damien Bell||1::Desmen Noble||2::Herman Seagers||3::Ladd Tate Sr||4::Tallis MooreNaN0::Injured, Unharmed, Arrested||1::Unharmed, Arrested||2::Killed||3::Injured||4::Injured0::Subject-Suspect||1::Subject-Suspect||2::Victim||3::Victim||4::Victimhttp://www.morningjournal.com/general-news/20130222/lorain-man-pleads-innocent-to-new-years-murder||http://chronicle.northcoastnow.com/2013/02/14/2-men-indicted-in-new-years-day-lorain-murder/56.013.0
34789252013-01-05ColoradoAurora16000 block of East Ithaca Place40http://www.gunviolencearchive.org/incident/478925http://www.dailydemocrat.com/20130106/aurora-shootout-killer-was-frenetic-talented-neighbor-saysFalse6.0NaNNaNShot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Drug involvement||Kidnapping/abductions/hostage||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )39.6518NaN-104.8020NaNNaN0::29||1::33||2::56||3::330::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+0::Female||1::Male||2::Male||3::Male0::Stacie Philbrook||1::Christopher Ratliffe||2::Anthony Ticali||3::Sonny ArchuletaNaN0::Killed||1::Killed||2::Killed||3::Killed0::Victim||1::Victim||2::Victim||3::Subject-Suspecthttp://denver.cbslocal.com/2013/01/06/officer-told-neighbor-standoff-gunman-was-on-meth-binge/||http://www.westword.com/news/sonny-archuleta-triple-murder-in-aurora-guns-purchased-legally-55-57-5900504||http://www.denverpost.com/ci_22322380/aurora-shooter-was-frenetic-talented-neighbor-says||http://www.dailymail.co.uk/news/article-2258008/Sonny-Archuleta-Gunman-left-dead-latest-Aurora-shooting-lost-brother-gun-violence.html||http://www.dailydemocrat.com/20130106/aurora-shootout-killer-was-frenetic-talented-neighbor-says40.028.0
44789592013-01-07North CarolinaGreensboro307 Mourning Dove Terrace22http://www.gunviolencearchive.org/incident/478959http://www.journalnow.com/news/local/article_d4c723e8-5a0f-11e2-a1fa-0019bb30f31a.htmlFalse6.00::Unknown||1::Unknown0::Handgun||1::HandgunShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Attempted Murder/Suicide (one variable unsuccessful)||Domestic Violence36.1140NaN-79.95692.0Two firearms recovered. (Attempted) murder suicide - both succeeded in fulfilling an M/S and did not succeed, based on details.0::18||1::46||2::14||3::470::Adult 18+||1::Adult 18+||2::Teen 12-17||3::Adult 18+0::Female||1::Male||2::Male||3::Female0::Danielle Imani Jameison||1::Maurice Eugene Edmonds, Sr.||2::Maurice Edmonds II||3::Sandra Palmer3::Family0::Injured||1::Injured||2::Killed||3::Killed0::Victim||1::Victim||2::Victim||3::Subject-Suspecthttp://myfox8.com/2013/01/08/update-mother-shot-14-year-old-son-two-others-before-killing-herself/||http://myfox8.com/2013/01/07/police-respond-to-report-of-triple-shooting-in-greensboro/||http://www.journalnow.com/news/local/article_d4c723e8-5a0f-11e2-a1fa-0019bb30f31a.html62.027.0
54789482013-01-07OklahomaTulsa6000 block of South Owasso40http://www.gunviolencearchive.org/incident/478948http://usnews.nbcnews.com/_news/2013/01/07/16397584-police-four-women-found-dead-in-tulsa-okla-apartment?liteFalse1.0NaNNaNShot - Dead (murder, accidental, suicide)||Home Invasion||Home Invasion - Resident killed||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Armed robbery with injury/death and/or evidence of DGU found36.2405Fairmont Terrace-95.9768NaNNaN0::23||1::23||2::33||3::550::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+||5::Adult 18+0::Female||1::Female||2::Female||3::Female||4::Male||5::Male0::Rebeika Powell||1::Kayetie Melchor||2::Misty Nunley||3::Julie Jackson||4::James Poore||5::Cedric PooreNaN0::Killed||1::Killed||2::Killed||3::Killed||4::Unharmed, Arrested||5::Unharmed, Arrested0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspect||5::Subject-Suspecthttp://www.kjrh.com/news/local-news/4-found-shot-inside-apartment-in-tulsa||http://www.cbsnews.com/news/tulsa-apartment-murders-update-hearing-scheduled-for-brothers-charged-in-quadruple-killing/||http://www.kjrh.com/news/local-news/hearing-continues-for-fairmont-terrace-quadruple-homicide-suspect-cedric-poore-james-poore||http://www.kjrh.com/news/local-news/hearing-for-quadruple-murder-suspects-continue||http://usnews.nbcnews.com/_news/2013/01/07/16397584-police-four-women-found-dead-in-tulsa-okla-apartment?lite72.011.0
64793632013-01-19New MexicoAlbuquerque2806 Long Lane50http://www.gunviolencearchive.org/incident/479363http://hinterlandgazette.com/2013/01/pastor-greg-griego-identified-victims-killed-nehemiah-griego-albuquerque-nm-shooting.htmlFalse1.00::Unknown||1::Unknown0::22 LR||1::223 Rem [AR-15]Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Domestic Violence34.9791NaN-106.71602.0NaN0::51||1::40||2::9||3::5||4::2||5::150::Adult 18+||1::Adult 18+||2::Child 0-11||3::Child 0-11||4::Child 0-11||5::Teen 12-170::Male||1::Female||2::Male||3::Female||4::Female||5::Male0::Greg Griego||1::Sara Griego||2::Zephania Griego||3::Jael Griego||4::Angelina Griego||5::Nehemiah Griego5::Family0::Killed||1::Killed||2::Killed||3::Killed||4::Killed||5::Unharmed, Arrested0::Victim||1::Victim||2::Victim||3::Victim||4::Victim||5::Subject-Suspecthttp://www.cbsnews.com/news/nehemiah-gringo-case-memorial-service-planned-for-family-allegedly-slain-by-new-mexico-teen/||http://www.thewire.com/national/2013/01/teenager-reportedly-used-ar-15-kill-five-new-mexico/61199/||http://bigstory.ap.org/article/officials-nm-teen-gunman-kills-5-inside-home||http://www.huffingtonpost.com/2013/01/21/nehemiah-griego-teen-shoots-parents-3-children_n_2519359.html||http://murderpedia.org/male.G/g/griego-nehemiah.htm||http://hinterlandgazette.com/2013/01/pastor-greg-griego-identified-victims-killed-nehemiah-griego-albuquerque-nm-shooting.html10.014.0
74793742013-01-21LouisianaNew OrleansLaSalle Street and Martin Luther King Jr. Boulevard05http://www.gunviolencearchive.org/incident/479374http://www.nola.com/crime/index.ssf/2013/01/nopd_4_people_shot_in_central.htmlFalse2.0NaNNaNShot - Wounded/Injured||Drive-by (car to street, car to car)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)29.9435NaN-90.0836NaNUnprovoked drive-by results in multiple teens and young adults injured. No source with names or exact ages.NaNNaN0::Male||1::Male||2::Male||3::Male||4::MaleNaNNaN0::Injured||1::Injured||2::Injured||3::Injured||4::Injured0::Victim||1::Victim||2::Victim||3::Victim||4::Victim||5::Subject-Suspecthttp://www.huffingtonpost.com/2013/01/21/new-orleans-mlk-day-shooting_n_2521750.html||http://www.fox8live.com/story/20640921/nopd-4-shot-alomg||http://www.nola.com/crime/index.ssf/2013/01/nopd_4_people_shot_in_central.html93.05.0
84793892013-01-21CaliforniaBrentwood1100 block of Breton Drive04http://www.gunviolencearchive.org/incident/479389http://sanfrancisco.cbslocal.com/2013/01/22/4-teens-hurt-in-drive-by-shooting-in-brentwood/False9.0NaNNaNShot - Wounded/Injured||Drive-by (car to street, car to car)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)||Gang involvement37.9656NaN-121.7180NaNPerps were likely motivated by gang affliationsNaN0::Teen 12-17||1::Teen 12-17||2::Teen 12-17||4::Adult 18+0::Male||1::Male||2::Male||3::Male||4::MaleNaNNaN0::Injured||1::Injured||2::Injured||3::Injured||4::Unharmed0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspecthttp://www.contracostatimes.com/ci_22426767/brentwood-teens-may-have-been-shot-over-nike||http://sanfrancisco.cbslocal.com/2013/01/22/4-teens-hurt-in-drive-by-shooting-in-brentwood/11.07.0
94921512013-01-23MarylandBaltimore1500 block of W. Fayette St.16http://www.gunviolencearchive.org/incident/492151http://www.abc2news.com/news/crime-checker/baltimore-city-crime/mother-of-murdered-15-yo-speaks-outFalse7.0NaNNaNShot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)39.2899NaN-76.6412NaNShooting occurred over illegal dice game; victim was killed as he sat on a stoop. Victim was not a participant in the game.0::150::Teen 12-17||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+||5::Adult 18+||6::Adult 18+0::Male0::Deshaun JonesNaN0::Killed||1::Injured||2::Injured||3::Injured||4::Injured||5::Injured||6::Injured0::Victim||1::Victim||2::Victim||3::Victim||4::Victim||5::Victim||6::Victimhttp://articles.baltimoresun.com/2013-08-25/news/bs-md-ci-shootings-20130824_1_west-baltimore-15-year-old-boy-shooting-incidents||http://baltimore.cbslocal.com/2013/08/25/juvenile-dead-6-shot-after-dice-game-goes-bad/||http://www.abc2news.com/news/crime-checker/baltimore-city-crime/mother-of-murdered-15-yo-speaks-outNaN44.0
incident_iddatestatecity_or_countyaddressn_killedn_injuredincident_urlsource_urlincident_url_fields_missingcongressional_districtgun_stolengun_typeincident_characteristicslatitudelocation_descriptionlongituden_guns_involvednotesparticipant_ageparticipant_age_groupparticipant_genderparticipant_nameparticipant_relationshipparticipant_statusparticipant_typesourcesstate_house_districtstate_senate_district
23966710822342018-03-31TennesseeMemphis2900 block of Wingate01http://www.gunviolencearchive.org/incident/1082234http://wreg.com/2018/03/31/one-person-in-critical-condition-after-a-shooting-near-frayser/False9.00::Unknown0::UnknownShot - Wounded/Injured||Domestic Violence35.2045NaN-89.98721.0NaN0::69||1::350::Adult 18+||1::Adult 18+0::Male||1::Female1::Nicole McKinneyNaN0::Injured||1::Arrested0::Victim||1::Subject-Suspecthttp://wreg.com/2018/03/31/one-person-in-critical-condition-after-a-shooting-near-frayser/90.030.0
23966810817422018-03-31MichiganDetroitI-9601http://www.gunviolencearchive.org/incident/1081742https://www.freep.com/story/news/local/michigan/wayne/2018/03/31/party-bus-shooting-detroit/475537002/FalseNaN0::Unknown0::UnknownShot - Wounded/Injured||Drive-by (car to street, car to car)NaNNaNNaN1.0near Wyoming StNaNNaN0::MaleNaNNaN0::Injured0::Victimhttps://www.freep.com/story/news/local/michigan/wayne/2018/03/31/party-bus-shooting-detroit/475537002/NaNNaN
23966910829902018-03-31WisconsinMadisonHayes Rd00http://www.gunviolencearchive.org/incident/1082990https://www.channel3000.com/news/crime/couple-finds-evidence-of-gunfire-after-reports-of-shots-fired-in-area/724111486FalseNaN0::Unknown0::45 AutoShots Fired - No InjuriesNaNNaNNaN1.0.45 shell casing recoveredNaNNaNNaNNaNNaNNaNNaNhttp://host.madison.com/wsj/news/local/crime/gunfire-reported-on-east-side-nobody-hurt-madison-police-say/article_a621b2db-60ff-5f00-94f6-b3aa80711e99.html||https://www.channel3000.com/news/crime/couple-finds-evidence-of-gunfire-after-reports-of-shots-fired-in-area/724111486NaNNaN
23967010817522018-03-31IllinoisChicago1 block of N Paulina St01http://www.gunviolencearchive.org/incident/1081752https://chicago.suntimes.com/news/man-36-wounded-in-shooting-on-near-west-side/FalseNaN0::Unknown0::UnknownShot - Wounded/InjuredNaNNaNNaN1.0Rt. leg, good; walk-up by 1;0::360::Adult 18+||1::Adult 18+0::Male||1::MaleNaNNaN0::Injured||1::Unharmed0::Victim||1::Subject-Suspecthttps://chicago.suntimes.com/news/man-36-wounded-in-shooting-on-near-west-side/NaNNaN
23967110820612018-03-31WashingtonSpokane (Spokane Valley)12600 block of N Willow Crest Ln00http://www.gunviolencearchive.org/incident/1082061https://www.kxly.com/news/domestic-violence-suspect-arrested-after-swat-team-standoff/723525275False5.00::Unknown0::UnknownNon-Shooting Incident||Possession (gun(s) found during commission of other crimes)47.6638NaN-117.23501.0DV call leads to seizure of firearms during arrest. No injuries.0::480::Adult 18+0::Male0::Sean M. GummowNaN0::Unharmed, Arrested0::Subject-Suspecthttps://www.kxly.com/news/domestic-violence-suspect-arrested-after-swat-team-standoff/7235252754.04.0
23967210831422018-03-31LouisianaRayneNorth Riceland Road and Highway 9000http://www.gunviolencearchive.org/incident/1083142http://www.klfy.com/news/local/rayne-woman-charged-with-attemped-murder-for-shooting-at-victim-trying-to-visit-children/1094165597FalseNaN0::Unknown0::UnknownShots Fired - No InjuriesNaNNaNNaN1.0NaN0::250::Adult 18+0::Female0::Jhkeya TezenoNaN0::Unharmed, Arrested0::Subject-Suspecthttp://www.klfy.com/news/local/rayne-woman-charged-with-attemped-murder-for-shooting-at-victim-trying-to-visit-children/1094165597NaNNaN
23967310831392018-03-31LouisianaNatchitoches247 Keyser Ave10http://www.gunviolencearchive.org/incident/1083139http://www.ksla.com/story/37854648/man-wanted-in-connection-with-natchitoches-parish-shooting-surrendersFalse4.00::Unknown0::UnknownShot - Dead (murder, accidental, suicide)||Institution/Group/Business31.7537Shop Rite-93.08361.0NaN1::210::Adult 18+||1::Adult 18+0::Male||1::Male0::Jamal Haskett||1::Jaquarious Tyjuan ArdisonNaN0::Killed||1::Unharmed, Arrested0::Victim||1::Subject-Suspecthttp://www.ksla.com/story/37854648/man-wanted-in-connection-with-natchitoches-parish-shooting-surrenders23.031.0
23967410831512018-03-31LouisianaGretna1300 block of Cook Street01http://www.gunviolencearchive.org/incident/1083151http://www.nola.com/crime/index.ssf/2018/04/shooting_reported_on_st_charle.html#incart_river_indexFalse2.00::Unknown0::UnknownShot - Wounded/Injured29.9239NaN-90.04421.0NaN0::210::Adult 18+0::MaleNaNNaN0::Injured0::Victimhttp://www.nola.com/crime/index.ssf/2018/04/shooting_reported_on_st_charle.html#incart_river_index85.07.0
23967510825142018-03-31TexasHouston12630 Ashford Point Dr10http://www.gunviolencearchive.org/incident/1082514https://www.chron.com/news/houston-texas/houston/article/Man-found-shot-in-car-in-Houston-s-Westside-12799287.phpFalse9.00::Unknown0::UnknownShot - Dead (murder, accidental, suicide)29.7201Vanderbilt Court apartments-95.61101.0Vic was found shot to death in car on 4/1/18, apartment complex0::420::Adult 18+0::Male0::Leroy EllisNaN0::Killed0::Victimhttp://www.khou.com/article/news/hpd-investigating-after-man-found-dead-in-car-on-easter-sunday/285-534039633||https://www.chron.com/news/houston-texas/houston/article/Man-found-shot-in-car-in-Houston-s-Westside-12799287.php149.017.0
23967610819402018-03-31MaineNorridgewock434 Skowhegan Rd20http://www.gunviolencearchive.org/incident/1081940https://www.centralmaine.com/2018/03/31/police-say-two-norridgewock-deaths-appear-to-be-murder-suicide/False2.00::Unknown||1::Unknown0::Handgun||1::ShotgunShot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence44.7293NaN-69.76912.0ALT: US 2, shot wife then self, handgun, shotgun recover from home0::58||1::620::Adult 18+||1::Adult 18+0::Female||1::Male0::Marie Lancaster Hale||1::William Hale1::Significant others - current or former0::Killed||1::Killed0::Victim||1::Subject-Suspecthttps://www.centralmaine.com/2018/03/31/police-say-two-norridgewock-deaths-appear-to-be-murder-suicide/111.03.0